Sensor-based object-detection optimization for autonomous vehicles

ABSTRACT

Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include receiving an indication of a sensor anomaly, determining one or more sensor recovery strategies based on the sensor anomaly, and executing a course of action that ensures the autonomous vehicle system operates within accepted parameters. Alternative sensors may be relied upon to cover for the sensor anomaly, which may include a failed sensor while the autonomous vehicle is in operation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/756,991, filed Nov. 4, 2015, entitled “SENSOR-BASED OBJECT-DETECTIONOPTIMIZATION FOR AUTONOMOUS VEHICLES,” the entirety of which isincorporated herein by reference.

FIELD

Various embodiments relate generally to autonomous vehicles andassociated mechanical, electrical and electronic hardware, computersoftware and systems, and wired and wireless network communications toprovide an autonomous vehicle fleet as a service.

BACKGROUND

A variety of approaches to developing driverless vehicles focuspredominately on automating conventional vehicles (e.g., manually-drivenautomotive vehicles) with an aim toward producing driverless vehiclesfor consumer purchase. For example, a number of automotive companies andaffiliates are modifying conventional automobiles and controlmechanisms, such as steering, to provide consumers with an ability toown a vehicle that may operate without a driver. In some approaches, aconventional driverless vehicle performs safety-critical drivingfunctions in some conditions, but requires a driver to assume control(e.g., steering, etc.) should the vehicle controller fail to resolvecertain issues that might jeopardize the safety of the occupants.

Although functional, conventional driverless vehicles typically have anumber of drawbacks. For example, a large number of driverless carsunder development have evolved from vehicles requiring manual (i.e.,human-controlled) steering and other like automotive functions.Therefore, a majority of driverless cars are based on a paradigm that avehicle is to be designed to accommodate a licensed driver, for which aspecific seat or location is reserved within the vehicle. As such,driverless vehicles are designed sub-optimally and generally foregoopportunities to simplify vehicle design and conserve resources (e.g.,reducing costs of producing a driverless vehicle). Other drawbacks arealso present in conventional driverless vehicles.

Other drawbacks are also present in conventional transportationservices, which are not well-suited for managing, for example, inventoryof vehicles effectively due to the common approaches of providingconventional transportation and ride-sharing services. In oneconventional approach, passengers are required to access a mobileapplication to request transportation services via a centralized servicethat assigns a human driver and vehicle (e.g., under private ownership)to a passenger. With the use of differently-owned vehicles, maintenanceof private vehicles and safety systems generally go unchecked. Inanother conventional approach, some entities enable ride-sharing for agroup of vehicles by allowing drivers, who enroll as members, access tovehicles that are shared among the members. This approach is notwell-suited to provide for convenient transportation services as driversneed to pick up and drop off shared vehicles at specific locations,which typically are rare and sparse in city environments, and requireaccess to relatively expensive real estate (i.e., parking lots) at whichto park ride-shared vehicles. In the above-described conventionalapproaches, the traditional vehicles used to provide transportationservices are generally under-utilized, from an inventory perspective, asthe vehicles are rendered immobile once a driver departs. Further,ride-sharing approaches (as well as individually-owned vehicletransportation services) generally are not well-suited to rebalanceinventory to match demand of transportation services to accommodateusage and typical travel patterns. Note, too, that someconventionally-described vehicles having limited self-driving automationcapabilities also are not well-suited to rebalance inventories as ahuman driver generally may be required. Examples of vehicles havinglimited self-driving automation capabilities are vehicles designated asLevel 3 (“L3”) vehicles, according to the U.S. Department ofTransportation's National Highway Traffic Safety Administration(“NHTSA”).

As another drawback, typical approaches to driverless vehicles aregenerally not well-suited to detect and navigate vehicles relative tointeractions (e.g., social interactions) between a vehicle-in-travel andother drivers of vehicles or individuals. For example, some conventionalapproaches are not sufficiently able to identify pedestrians, cyclists,etc., and associated interactions, such as eye contact, gesturing, andthe like, for purposes of addressing safety risks to occupants of adriverless vehicles, as well as drivers of other vehicles, pedestrians,etc.

Thus, what is needed is a solution for facilitating an implementation ofautonomous vehicles, without the limitations of conventional techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments or examples (“examples”) of the invention aredisclosed in the following detailed description and the accompanyingdrawings:

FIG. 1 is a diagram depicting implementation of a fleet of autonomousvehicles that are communicatively networked to an autonomous vehicleservice platform, according to some embodiments;

FIG. 2 is an example of a flow diagram to monitor a fleet of autonomousvehicles, according to some embodiments;

FIG. 3A is a diagram depicting examples of sensors and other autonomousvehicle components, according to some examples;

FIGS. 3B to 3E are diagrams depicting examples of sensor fieldredundancy and autonomous vehicle adaption to a loss of a sensor field,according to some examples;

FIG. 4 is a functional block diagram depicting a system including anautonomous vehicle service platform that is communicatively coupled viaa communication layer to an autonomous vehicle controller, according tosome examples;

FIG. 5 is an example of a flow diagram to control an autonomous vehicle,according to some embodiments;

FIG. 6 is a diagram depicting an example of an architecture for anautonomous vehicle controller, according to some embodiments;

FIG. 7 is a diagram depicting an example of an autonomous vehicleservice platform implementing redundant communication channels tomaintain reliable communications with a fleet of autonomous vehicles,according to some embodiments;

FIG. 8 is a diagram depicting an example of a messaging applicationconfigured to exchange data among various applications, according tosome embodiment;

FIG. 9 is a diagram depicting types of data for facilitatingteleoperations using a communications protocol described in FIG. 8,according to some examples;

FIG. 10 is a diagram illustrating an example of a teleoperator interfacewith which a teleoperator may influence path planning, according to someembodiments;

FIG. 11 is a diagram depicting an example of a planner configured toinvoke teleoperations, according to some examples;

FIG. 12 is an example of a flow diagram configured to control anautonomous vehicle, according to some embodiments;

FIG. 13 depicts an example in which a planner may generate a trajectory,according to some examples;

FIG. 14 is a diagram depicting another example of an autonomous vehicleservice platform, according to some embodiments;

FIG. 15 is an example of a flow diagram to control an autonomousvehicle, according to some embodiments;

FIG. 16 is a diagram of an example of an autonomous vehicle fleetmanager implementing a fleet optimization manager, according to someexamples;

FIG. 17 is an example of a flow diagram for managing a fleet ofautonomous vehicles, according to some embodiments;

FIG. 18 is a diagram illustrating an autonomous vehicle fleet managerimplementing an autonomous vehicle communications link manager,according to some embodiments;

FIG. 19 is an example of a flow diagram to determine actions forautonomous vehicles during an event, according to some embodiments;

FIG. 20 is a diagram depicting an example of a localizer, according tosome embodiments;

FIG. 21 is an example of a flow diagram to generate local pose databased on integrated sensor data, according to some embodiments;

FIG. 22 is a diagram depicting another example of a localizer, accordingto some embodiments;

FIG. 23 is a diagram depicting an example of a perception engine,according to some embodiments;

FIG. 24 is an example of a flow chart to generate perception enginedata, according to some embodiments;

FIG. 25 is an example of a segmentation processor, according to someembodiments;

FIG. 26A is a diagram depicting examples of an object tracker and aclassifier, according to various embodiments;

FIG. 26B is a diagram depicting another example of an object trackeraccording to at least some examples;

FIG. 27 is an example of front-end processor for a perception engine,according to some examples;

FIG. 28 is a diagram depicting a simulator configured to simulate anautonomous vehicle in a synthetic environment, according to variousembodiments;

FIG. 29 is an example of a flow chart to simulate various aspects of anautonomous vehicle, according to some embodiments;

FIG. 30 is an example of a flow chart to generate map data, according tosome embodiments;

FIG. 31 is a diagram depicting an architecture of a mapping engine,according to some embodiments

FIG. 32 is a diagram depicting an autonomous vehicle application,according to some examples;

FIGS. 33 to 35 illustrate examples of various computing platformsconfigured to provide various functionalities to components of anautonomous vehicle service, according to various embodiments;

FIGS. 36A to 36B illustrate a high-level block diagram depicting anautonomous vehicle system having a sensor-based anomaly while inoperation, according to various embodiments;

FIG. 37 illustrates a high-level block diagram of a sensor-based objectdetection optimization for autonomous vehicles, according to variousembodiments;

FIG. 38 is a network diagram of a system for sensor-based objectdetection optimization for autonomous vehicles, showing a block diagramof an autonomous vehicle management system, according to an embodiment;

FIG. 39 is a high-level flow diagram illustrating a process forsensor-based object detection optimization for autonomous vehicles,according to some examples; and

FIGS. 40 and 41 illustrate exemplary computing platforms disposed indevices configured to optimize sensor-based object detection inaccordance with various embodiments.

DETAILED DESCRIPTION

Various embodiments or examples may be implemented in numerous ways,including as a system, a process, an apparatus, a user interface, or aseries of program instructions on a computer readable medium such as acomputer readable storage medium or a computer network where the programinstructions are sent over optical, electronic, or wirelesscommunication links. In general, operations of disclosed processes maybe performed in an arbitrary order, unless otherwise provided in theclaims.

A detailed description of one or more examples is provided below alongwith accompanying figures. The detailed description is provided inconnection with such examples, but is not limited to any particularexample. The scope is limited only by the claims, and numerousalternatives, modifications, and equivalents thereof. Numerous specificdetails are set forth in the following description in order to provide athorough understanding. These details are provided for the purpose ofexample and the described techniques may be practiced according to theclaims without some or all of these specific details. For clarity,technical material that is known in the technical fields related to theexamples has not been described in detail to avoid unnecessarilyobscuring the description.

FIG. 1 is a diagram depicting an implementation of a fleet of autonomousvehicles that are communicatively networked to an autonomous vehicleservice platform, according to some embodiments. Diagram 100 depicts afleet of autonomous vehicles 109 (e.g., one or more of autonomousvehicles 109 a to 109 e) operating as a service, each autonomous vehicle109 being configured to self-drive a road network 110 and establish acommunication link 192 with an autonomous vehicle service platform 101.In examples in which a fleet of autonomous vehicles 109 constitutes aservice, a user 102 may transmit a request 103 for autonomoustransportation via one or more networks 106 to autonomous vehicleservice platform 101. In response, autonomous vehicle service platform101 may dispatch one of autonomous vehicles 109 to transport user 102autonomously from geographic location 119 to geographic location 111.Autonomous vehicle service platform 101 may dispatch an autonomousvehicle from a station 190 to geographic location 119, or may divert anautonomous vehicle 109 c, already in transit (e.g., without occupants),to service the transportation request for user 102. Autonomous vehicleservice platform 101 may be further configured to divert an autonomousvehicle 109 c in transit, with passengers, responsive to a request fromuser 102 (e.g., as a passenger). In addition, autonomous vehicle serviceplatform 101 may be configured to reserve an autonomous vehicle 109 c intransit, with passengers, for diverting to service a request of user 102subsequent to dropping off existing passengers. Note that multipleautonomous vehicle service platforms 101 (not shown) and one or morestations 190 may be implemented to service one or more autonomousvehicles 109 in connection with road network 110. One or more stations190 may be configured to store, service, manage, and/or maintain aninventory of autonomous vehicles 109 (e.g., station 190 may include oneor more computing devices implementing autonomous vehicle serviceplatform 101).

According to some examples, at least some of autonomous vehicles 109 ato 109 e are configured as bidirectional autonomous vehicles, such asbidirectional autonomous vehicle (“AV”) 130. Bidirectional autonomousvehicle 130 may be configured to travel in either direction principallyalong, but not limited to, a longitudinal axis 131. Accordingly,bidirectional autonomous vehicle 130 may be configured to implementactive lighting external to the vehicle to alert others (e.g., otherdrivers, pedestrians, cyclists, etc.) in the adjacent vicinity, and adirection in which bidirectional autonomous vehicle 130 is traveling.For example, active sources of light 136 may be implemented as activelights 138 a when traveling in a first direction, or may be implementedas active lights 138 b when traveling in a second direction. Activelights 138 a may be implemented using a first subset of one or morecolors, with optional animation (e.g., light patterns of variableintensities of light or color that may change over time). Similarly,active lights 138 b may be implemented using a second subset of one ormore colors and light patterns that may be different than those ofactive lights 138 a. For example, active lights 138 a may be implementedusing white-colored lights as “headlights,” whereas active lights 138 bmay be implemented using red-colored lights as “taillights.” Activelights 138 a and 138 b, or portions thereof, may be configured toprovide other light-related functionalities, such as provide “turnsignal indication” functions (e.g., using yellow light). According tovarious examples, logic in autonomous vehicle 130 may be configured toadapt active lights 138 a and 138 b to comply with various safetyrequirements and traffic regulations or laws for any number ofjurisdictions.

In some embodiments, bidirectional autonomous vehicle 130 may beconfigured to have similar structural elements and components in eachquad portion, such as quad portion 194. The quad portions are depicted,at least in this example, as portions of bidirectional autonomousvehicle 130 defined by the intersection of a plane 132 and a plane 134,both of which pass through the vehicle to form two similar halves oneach side of planes 132 and 134. Further, bidirectional autonomousvehicle 130 may include an autonomous vehicle controller 147 thatincludes logic (e.g., hardware or software, or as combination thereof)that is configured to control a predominate number of vehicle functions,including driving control (e.g., propulsion, steering, etc.) and activesources 136 of light, among other functions. Bidirectional autonomousvehicle 130 also includes a number of sensors 139 disposed at variouslocations on the vehicle (other sensors are not shown).

Autonomous vehicle controller 147 may be further configured to determinea local pose (e.g., local position) of an autonomous vehicle 109 and todetect external objects relative to the vehicle. For example, considerthat bidirectional autonomous vehicle 130 is traveling in the direction119 in road network 110. A localizer (not shown) of autonomous vehiclecontroller 147 can determine a local pose at the geographic location111. As such, the localizer may use acquired sensor data, such as sensordata associated with surfaces of buildings 115 and 117, which can becompared against reference data, such as map data (e.g., 3D map data,including reflectance data) to determine a local pose. Further, aperception engine (not shown) of autonomous vehicle controller 147 maybe configured to detect, classify, and predict the behavior of externalobjects, such as external object 112 (a “tree”) and external object 114(a “pedestrian”). Classification of such external objects may broadlyclassify objects as static objects, such as external object 112, anddynamic objects, such as external object 114. The localizer and theperception engine, as well as other components of the AV controller 147,collaborate to cause autonomous vehicles 109 to drive autonomously.

According to some examples, autonomous vehicle service platform 101 isconfigured to provide teleoperator services should an autonomous vehicle109 request teleoperation. For example, consider that an autonomousvehicle controller 147 in autonomous vehicle 109 d detects an object 126obscuring a path 124 on roadway 122 at point 191, as depicted in inset120. If autonomous vehicle controller 147 cannot ascertain a path ortrajectory over which vehicle 109 d may safely transit with a relativelyhigh degree of certainty, then autonomous vehicle controller 147 maytransmit request message 105 for teleoperation services. In response, ateleoperator computing device 104 may receive instructions from ateleoperator 108 to perform a course of action to successfully (andsafely) negotiate obstacles 126. Response data 107 then can betransmitted back to autonomous vehicle 109 d to cause the vehicle to,for example, safely cross a set of double lines as it transits along thealternate path 121. In some examples, teleoperator computing device 104may generate a response identifying geographic areas to exclude fromplanning a path. In particular, rather than provide a path to follow, ateleoperator 108 may define areas or locations that the autonomousvehicle must avoid.

In view of the foregoing, the structures and/or functionalities ofautonomous vehicle 130 and/or autonomous vehicle controller 147, as wellas their components, can perform real-time (or near real-time)trajectory calculations through autonomous-related operations, such aslocalization and perception, to enable autonomous vehicles 109 toself-drive.

In some cases, the bidirectional nature of bidirectional autonomousvehicle 130 provides for a vehicle that has quad portions 194 (or anyother number of symmetric portions) that are similar or aresubstantially similar to each other. Such symmetry reduces complexity ofdesign and decreases relatively the number of unique components orstructures, thereby reducing inventory and manufacturing complexities.For example, a drivetrain and wheel system may be disposed in any of thequad portions 194. Further, autonomous vehicle controller 147 isconfigured to invoke teleoperation services to reduce the likelihoodthat an autonomous vehicle 109 is delayed in transit while resolving anevent or issue that may otherwise affect the safety of the occupants. Insome cases, the visible portion of road network 110 depicts a geo-fencedregion that may limit or otherwise control the movement of autonomousvehicles 109 to the road network shown in FIG. 1. According to variousexamples, autonomous vehicle 109, and a fleet thereof, may beconfigurable to operate as a level 4 (“full self-driving automation,” orL4) vehicle that can provide transportation on demand with theconvenience and privacy of point-to-point personal mobility whileproviding the efficiency of shared vehicles. In some examples,autonomous vehicle 109, or any autonomous vehicle described herein, maybe configured to omit a steering wheel or any other mechanical means ofproviding manual (i.e., human-controlled) steering for autonomousvehicle 109. Further, autonomous vehicle 109, or any autonomous vehicledescribed herein, may be configured to omit a seat or location reservedwithin the vehicle for an occupant to engage a steering wheel.

FIG. 2 is an example of a flow diagram to monitor a fleet of autonomousvehicles, according to some embodiments. At 202, flow 200 begins when afleet of autonomous vehicles are monitored. At least one autonomousvehicle includes an autonomous vehicle controller configured to causethe vehicle to autonomously transit from a first geographic region to asecond geographic region. At 204, data representing an event associatedwith a calculated confidence level for a vehicle is detected. An eventmay be a condition or situation affecting operation, or potentiallyaffecting operation, of an autonomous vehicle. The events may beinternal to an autonomous vehicle, or external. For example, an obstacleobscuring a roadway may be viewed as an event, as well as a reduction orloss of communication. An event may include traffic conditions orcongestion, as well as unexpected or unusual numbers or types ofexternal objects (or tracks) that are perceived by a perception engine.An event may include weather-related conditions (e.g., loss of frictiondue to ice or rain) or the angle at which the sun is shining (e.g., atsunset), such as low angle to the horizon that cause sun to shinebrightly in the eyes of human drivers of other vehicles. These and otherconditions may be viewed as events that cause invocation of theteleoperator service or for the vehicle to execute a safe-stoptrajectory.

At 206, data representing a subset of candidate trajectories may bereceived from an autonomous vehicle responsive to the detection of theevent. For example, a planner of an autonomous vehicle controller maycalculate and evaluate large numbers of trajectories (e.g., thousands orgreater) per unit time, such as a second. In some embodiments, candidatetrajectories are a subset of the trajectories that provide forrelatively higher confidence levels that an autonomous vehicle may moveforward safely in view of the event (e.g., using an alternate pathprovided by a teleoperator). Note that some candidate trajectories maybe ranked or associated with higher degrees of confidence than othercandidate trajectories. According to some examples, subsets of candidatetrajectories may originate from any number of sources, such as aplanner, a teleoperator computing device (e.g., teleoperators candetermine and provide approximate paths), etc., and may be combined as asuperset of candidate trajectories. At 208, path guidance data may beidentified at one or more processors. The path guidance data may beconfigured to assist a teleoperator in selecting a guided trajectoryfrom one or more of the candidate trajectories. In some instances, thepath guidance data specifies a value indicative of a confidence level orprobability that indicates the degree of certainty that a particularcandidate trajectory may reduce or negate the probability that the eventmay impact operation of an autonomous vehicle. A guided trajectory, as aselected candidate trajectory, may be received at 210, responsive toinput from a teleoperator (e.g., a teleoperator may select at least onecandidate trajectory as a guided trajectory from a group ofdifferently-ranked candidate trajectories). The selection may be madevia an operator interface that lists a number of candidate trajectories,for example, in order from highest confidence levels to lowestconfidence levels. At 212, the selection of a candidate trajectory as aguided trajectory may be transmitted to the vehicle, which, in turn,implements the guided trajectory for resolving the condition by causingthe vehicle to perform a teleoperator-specified maneuver. As such, theautonomous vehicle may transition from a non-normative operationalstate.

FIG. 3A is a diagram depicting examples of sensors and other autonomousvehicle components, according to some examples. Diagram 300 depicts aninterior view of a bidirectional autonomous vehicle 330 that includessensors, signal routers 345, drive trains 349, removable batteries 343,audio generators 344 (e.g., speakers or transducers), and autonomousvehicle (“AV”) control logic 347. Sensors shown in diagram 300 includeimage capture sensors 340 (e.g., light capture devices or cameras of anytype), audio capture sensors 342 (e.g., microphones of any type), radardevices 348, sonar devices 341 (or other like sensors, includingultrasonic sensors or acoustic-related sensors), and LIDAR devices 346,among other sensor types and modalities (some of which are not shown,such inertial measurement units, or “IMUs,” global positioning system(“GPS”) sensors, sonar sensors, etc.). Note that quad portion 350 isrepresentative of the symmetry of each of four “quad portions” ofbidirectional autonomous vehicle 330 (e.g., each quad portion 350 mayinclude a wheel, a drivetrain 349, similar steering mechanisms, similarstructural support and members, etc. beyond that which is depicted). Asdepicted in FIG. 3A, similar sensors may be placed in similar locationsin each quad portion 350, however any other configuration mayimplemented. Each wheel may be steerable individually and independent ofthe others. Note, too, that removable batteries 343 may be configured tofacilitate being swapped in and swapped out rather than charging insitu, thereby ensuring reduced or negligible downtimes due to thenecessity of charging batteries 343. While autonomous vehicle controlleris depicted as being used in a bidirectional autonomous vehicle 330,autonomous vehicle controller is not so limited and may be implementedin unidirectional autonomous vehicles or any other type of vehicle,whether on land, in air, or at sea. Note that the depicted and describedpositions, locations, orientations, quantities, and types of sensorsshown in FIG. 3A are not intended to be limiting, and, as such, theremay be any number and type of sensor, and any sensor may be located andoriented anywhere on autonomous vehicle 330.

According to some embodiments, portions of the autonomous vehicle (“AV”)control logic 347 may be implemented using clusters of graphicsprocessing units (“GPUs”) implementing a framework and programming modelsuitable for programming the clusters of GPUs. For example, a computeunified device architecture (“CUDA”) compatible programming language andapplication programming interface (“API”) model may be used to programthe GPUs. CUDA™ is produced and maintained by NVIDIA of Santa Clara,Calif. Note that other programming languages may be implemented, such asOpenCL, or any other parallel programming language.

According to some embodiments, autonomous vehicle control logic 347 maybe implemented in hardware and/or software as autonomous vehiclecontroller 347 a, which is shown to include a motion controller 362, aplanner 364, a perception engine 366, and a localizer 368. As shown,autonomous vehicle controller 347 a is configured to receive camera data340 a. LIDAR data 346 a, and radar data 348 a, or any otherrange-sensing or localization data, including sonar data 341 a or thelike. Autonomous vehicle controller 347 a is also configured to receivepositioning data, such as GPS data 352, IMU data 354, and otherposition-sensing data (e.g., wheel-related data, such as steeringangles, angular velocity, etc.). Further, autonomous vehicle controller347 a may receive any other sensor data 356, as well as reference data339. In some cases, reference data 339 includes map data (e.g., 3D mapdata, 2D map data, 4D map data (e.g., including Epoch Determination))and route data (e.g., road network data, including, but not limited to,RNDF data (or similar data), MDF data (or similar data), etc.

Localizer 368 is configured to receive sensor data from one or moresources, such as GPS data 352, wheel data, IMU data 354, LIDAR data 346a, camera data 340 a, radar data 348 a, and the like, as well asreference data 339 (e.g., 3D map data and route data). Localizer 368integrates (e.g., fuses the sensor data) and analyzes the data bycomparing sensor data to map data to determine a local pose (orposition) of bidirectional autonomous vehicle 330. According to someexamples, localizer 368 may generate or update the pose or position ofany autonomous vehicle in real-time or near real-time. Note thatlocalizer 368 and its functionality need not be limited to“bi-directional” vehicles and can be implemented in any vehicle of anytype. Therefore, localizer 368 (as well as other components of AVcontroller 347 a) may be implemented in a “unidirectional” vehicle orany non-autonomous vehicle. According to some embodiments, datadescribing a local pose may include one or more of an x-coordinate, ay-coordinate, a z-coordinate (or any coordinate of any coordinatesystem, including polar or cylindrical coordinate systems, or the like),a yaw value, a roll value, a pitch value (e.g., an angle value), a rate(e.g., velocity), altitude, and the like.

Perception engine 366 is configured to receive sensor data from one ormore sources, such as LIDAR data 346 a, camera data 340 a, radar data348 a, and the like, as well as local pose data. Perception engine 366may be configured to determine locations of external objects based onsensor data and other data. External objects, for instance, may beobjects that are not part of a drivable surface. For example, perceptionengine 366 may be able to detect and classify external objects aspedestrians, bicyclists, dogs, other vehicles, etc. (e.g., perceptionengine 366 is configured to classify the objects in accordance with atype of classification, which may be associated with semanticinformation, including a label). Based on the classification of theseexternal objects, the external objects may be labeled as dynamic objectsor static objects. For example, an external object classified as a treemay be labeled as a static object, while an external object classifiedas a pedestrian may be labeled as a static object. External objectslabeled as static mayor may not be described in map data. Examples ofexternal objects likely to be labeled as static include traffic cones,cement barriers arranged across a roadway, lane closure signs,newly-placed mailboxes or trash cans adjacent a roadway, etc. Examplesof external objects likely to be labeled as dynamic include bicyclists,pedestrians, animals, other vehicles, etc. If the external object islabeled as dynamic, and further data about the external object mayindicate a typical level of activity and velocity, as well as behaviorpatterns associated with the classification type. Further data about theexternal object may be generated by tracking the external object. Assuch, the classification type can be used to predict or otherwisedetermine the likelihood that an external object may, for example,interfere with an autonomous vehicle traveling along a planned path. Forexample, an external object that is classified as a pedestrian may beassociated with some maximum speed, as well as an average speed (e.g.,based on tracking data). The velocity of the pedestrian relative to thevelocity of an autonomous vehicle can be used to determine if acollision is likely. Further, perception engine 364 may determine alevel of uncertainty associated with a current and future state ofobjects. In some examples, the level of uncertainty may be expressed asan estimated value (or probability).

Planner 364 is configured to receive perception data from perceptionengine 366, and may also include localizer data from localizer 368.According to some examples, the perception data may include an obstaclemap specifying static and dynamic objects located in the vicinity of anautonomous vehicle, whereas the localizer data may include a local poseor position. In operation, planner 364 generates numerous trajectories,and evaluates the trajectories, based on at least the location of theautonomous vehicle against relative locations of external dynamic andstatic objects. Planner 364 selects an optimal trajectory based on avariety of criteria over which to direct the autonomous vehicle in waythat provides for collision-free travel. In some examples, planner 364may be configured to calculate the trajectories asprobabilistically-determined trajectories. Further, planner 364 maytransmit steering and propulsion commands (as well as decelerating orbraking commands) to motion controller 362. Motion controller 362subsequently may convert any of the commands, such as a steeringcommand, a throttle or propulsion command, and a braking command, intocontrol signals (e.g., for application to actuators or other mechanicalinterfaces) to implement changes in steering or wheel angles 351 and/orvelocity 353.

FIGS. 3B to 3E are diagrams depicting examples of sensor fieldredundancy and autonomous vehicle adaption to a loss of a sensor field,according to some examples. Diagram 391 of FIG. 3B depicts a sensorfield 301 a in which sensor 310 a detects objects (e.g., for determiningrange or distance, or other information). While sensor 310 a mayimplement any type of sensor or sensor modality, sensor 310 a andsimilarly-described sensors, such as sensors 310 b, 310 c, and 310 d,may include LIDAR devices. Therefore, sensor fields 301 a, 301 b, 301 c,and 301 d each includes a field into which lasers extend. Diagram 392 ofFIG. 3C depicts four overlapping sensor fields each of which isgenerated by a corresponding LIDAR sensor 310 (not shown). As shown,portions 301 of the sensor fields include no overlapping sensor fields(e.g., a single LIDAR field), portions 302 of the sensor fields includetwo overlapping sensor fields, and portions 303 include threeoverlapping sensor fields, whereby such sensors provide for multiplelevels of redundancy should a LIDAR sensor fail.

FIG. 3D depicts a loss of a sensor field due to failed operation ofLIDAR 309, according to some examples. Sensor field 302 of FIG. 3C istransformed into a single sensor field 305, one of sensor fields 301 ofFIG. 3C is lost to a gap 304, and three of sensor fields 303 of FIG. 3Care transformed into sensor fields 306 (i.e., limited to two overlappingfields). Should autonomous car 330 c be traveling in the direction oftravel 396, the sensor field in front of the moving autonomous vehiclemay be less robust than the one at the trailing end portion. Accordingto some examples, an autonomous vehicle controller (not shown) isconfigured to leverage the bidirectional nature of autonomous vehicle330 c to address the loss of sensor field at the leading area in frontof the vehicle. FIG. 3E depicts a bidirectional maneuver for restoring acertain robustness of the sensor field in front of autonomous vehicle330 d. As shown, a more robust sensor field 302 is disposed at the rearof the vehicle 330 d coextensive with taillights 348. When convenient,autonomous vehicle 330 d performs a bidirectional maneuver by pullinginto a driveway 397 and switches its directionality such that taillights348 actively switch to the other side (e.g., the trailing edge) ofautonomous vehicle 330 d. As shown, autonomous vehicle 330 d restores arobust sensor field 302 in front of the vehicle as it travels alongdirection of travel 398. Further, the above-described bidirectionalmaneuver obviates a requirement for a more complicated maneuver thatrequires backing up into a busy roadway.

FIG. 4 is a functional block diagram depicting a system including anautonomous vehicle service platform that is communicatively coupled viaa communication layer to an autonomous vehicle controller, according tosome examples. Diagram 400 depicts an autonomous vehicle controller(“AV”) 447 disposed in an autonomous vehicle 430, which, in turn,includes a number of sensors 470 coupled to autonomous vehiclecontroller 447. Sensors 470 include one or more LIDAR devices 472, oneor more cameras 474, one or more radars 476, one or more globalpositioning system (“GPS”) data receiver-sensors, one or more inertialmeasurement units (“IMUs”) 475, one or more odometry sensors 477 (e.g.,wheel encoder sensors, wheel speed sensors, and the like), and any othersuitable sensors 478, such as infrared cameras or sensors,hyperspectral-capable sensors, ultrasonic sensors (or any other acousticenergy-based sensor), radio frequency-based sensors, etc. In some cases,wheel angle sensors configured to sense steering angles of wheels may beincluded as odometry sensors 477 or suitable sensors 478. In anon-limiting example, autonomous vehicle controller 447 may include fouror more LIDARs 472, sixteen or more cameras 474 and four or more radarunits 476. Further, sensors 470 may be configured to provide sensor datato components of autonomous vehicle controller 447 and to elements ofautonomous vehicle service platform 401. As shown in diagram 400,autonomous vehicle controller 447 includes a planner 464, a motioncontroller 462, a localizer 468, a perception engine 466, and a localmap generator 440. Note that elements depicted in diagram 400 of FIG. 4may include structures and/or functions as similarly-named elementsdescribed in connection to one or more other drawings.

Localizer 468 is configured to localize autonomous vehicle (i.e.,determine a local pose) relative to reference data, which may includemap data, route data (e.g., road network data, such as RNOF-like data),and the like. In some cases, localizer 468 is configured to identify,for example, a point in space that may represent a location ofautonomous vehicle 430 relative to features of a representation of anenvironment. Localizer 468 is shown to include a sensor data integrator469, which may be configured to integrate multiple subsets of sensordata (e.g., of different sensor modalities) to reduce uncertaintiesrelated to each individual type of sensor. According to some examples,sensor data integrator 469 is configured to fuse sensor data (e.g.,LIDAR data, camera data, radar data, etc.) to form integrated sensordata values for determining a local pose. According to some examples,localizer 468 retrieves reference data originating from a reference datarepository 405, which includes a map data repository 405 a for storing2D map data, 3D map data, 4D map data, and the like. Localizer 468 maybe configured to identify at least a subset of features in theenvironment to match against map data to identify, or otherwise confirm,a pose of autonomous vehicle 430. According to some examples, localizer468 may be configured to identify any amount of features in anenvironment, such that a set of features can one or more features, orall features. In a specific example, any amount of LIDAR data (e.g.,most or substantially all LIDAR data) may be compared against datarepresenting a map for purposes of localization. Generally, non-matchedobjects resulting from the comparison of the environment features andmap data may be a dynamic object, such as a vehicle, bicyclist,pedestrian, etc. Note that detection of dynamic objects, includingobstacles, may be performed with or without map data. In particular,dynamic objects may be detected and tracked independently of map data(i.e., in the absence of map data). In some instances, 2D map data and3D map data may be viewed as “global map data” or map data that has beenvalidated at a point in time by autonomous vehicle service platform 401.As map data in map data repository 405 a may be updated and/or validatedperiodically, a deviation may exist between the map data and an actualenvironment in which the autonomous vehicle is positioned. Therefore,localizer 468 may retrieve locally-derived map data generated by localmap generator 440 to enhance localization. Local map generator 440 isconfigured to generate local map data in real-time or near real-time.Optionally, local map generator 440 may receive static and dynamicobject map data to enhance the accuracy of locally generated maps by,for example, disregarding dynamic objects in localization. According toat least some embodiments, local map generator 440 may be integratedwith, or formed as part of, localizer 468. In at least one case, localmap generator 440, either individually or in collaboration withlocalizer 468, may be configured to generate map and/or reference databased on simultaneous localization and mapping (“SLAM”) or the like.Note that localizer 468 may implement a “hybrid” approach to using mapdata, whereby logic in localizer 468 may be configured to select variousamounts of map data from either map data repository 405 a or local mapdata from local map generator 440, depending on the degrees ofreliability of each source of map data. Therefore, localizer 468 maystill use out-of-date map data in view of locally-generated map data.

Perception engine 466 is configured to, for example, assist planner 464in planning routes and generating trajectories by identifying objects ofinterest in a surrounding environment in which autonomous vehicle 430 istransiting. Further, probabilities may be associated with each of theobject of interest, whereby a probability may represent a likelihoodthat an object of interest may be a threat to safe travel (e.g., afast-moving motorcycle may require enhanced tracking rather than aperson sitting at a bus stop bench while reading a newspaper). As shown,perception engine 466 includes an object detector 442 and an objectclassifier 444. Object detector 442 is configured to distinguish objectsrelative to other features in the environment, and object classifier 444may be configured to classify objects as either dynamic or staticobjects and track the locations of the dynamic and the static objectsrelative to autonomous vehicle 430 for planning purposes. Further,perception engine 466 may be configured to assign an identifier to astatic or dynamic object that specifies whether the object is (or hasthe potential to become) an obstacle that may impact path planning atplanner 464. Although not shown in FIG. 4, note that perception engine466 may also perform other perception-related functions, such assegmentation and tracking, examples of which are described below.

Planner 464 is configured to generate a number of candidate trajectoriesfor accomplishing a goal to reaching a destination via a number of pathsor routes that are available. Trajectory evaluator 465 is configured toevaluate candidate trajectories and identify which subsets of candidatetrajectories are associated with higher degrees of confidence levels ofproviding collision-free paths to the destination. As such, trajectoryevaluator 465 can select an optimal trajectory based on relevantcriteria for causing commands to generate control signals for vehiclecomponents 450 (e.g., actuators or other mechanisms). Note that therelevant criteria may include any number of factors that define optimaltrajectories, the selection of which need not be limited to reducingcollisions. For example, the selection of trajectories may be made tooptimize user experience (e.g., user comfort) as well as collision-freetrajectories that comply with traffic regulations and laws. Userexperience may be optimized by moderating accelerations in variouslinear and angular directions (e.g., to reduce jerking-like travel orother unpleasant motion). In some cases, at least a portion of therelevant criteria can specify which of the other criteria to override orsupersede, while maintain optimized, collision-free travel. For example,legal restrictions may be temporarily lifted or deemphasized whengenerating trajectories in limited situations (e.g., crossing doubleyellow lines to go around a cyclist or travelling at higher speeds thanthe posted speed limit to match traffic flows). As such, the controlsignals are configured to cause propulsion and directional changes atthe drivetrain and/or wheels. In this example, motion controller 462 isconfigured to transform commands into control signals (e.g., velocity,wheel angles, etc.) for controlling the mobility of autonomous vehicle430. In the event that trajectory evaluator 465 has insufficientinformation to ensure a confidence level high enough to providecollision-free, optimized travel, planner 464 can generate a request toteleoperator 404 for teleoperator support.

Autonomous vehicle service platform 401 includes teleoperator 404 (e.g.,a teleoperator computing device), reference data repository 405, a mapupdater 406, a vehicle data controller 408, a calibrator 409, and anoff-line object classifier 410. Note that each element of autonomousvehicle service platform 401 may be independently located or distributedand in communication with other elements in autonomous vehicle serviceplatform 401. Further, element of autonomous vehicle service platform401 may independently communicate with the autonomous vehicle 430 viathe communication layer 402. Map updater 406 is configured to receivemap data (e.g., from local map generator 440, sensors 460, or any othercomponent of autonomous vehicle controller 447), and is furtherconfigured to detect deviations, for example, of map data in map datarepository 405 a from a locally-generated map. Vehicle data controller408 can cause 2D map updater 406 to update reference data withinrepository 405 and facilitate updates to 2D, 3D, and/or 4D map data. Insome cases, vehicle data controller 408 can control the rate at whichlocal map data is received into autonomous vehicle service platform 408as well as the frequency at which map updater 406 performs updating ofthe map data.

Calibrator 409 is configured to perform calibration of various sensorsof the same or different types. Calibrator 409 may be configured todetermine the relative poses of the sensors (e.g., in Cartesian space(x, y, z)) and orientations of the sensors (e.g., roll, pitch and yaw).The pose and orientation of a sensor, such a camera, LIDAR sensor, radarsensor, etc., may be calibrated relative to other sensors, as well asglobally relative to the vehicle's reference frame. Off-lineself-calibration can also calibrate or estimate other parameters, suchas vehicle inertial tensor, wheel base, wheel radius or surface roadfriction. Calibration can also be done online to detect parameterchange, according to some examples. Note, too, that calibration bycalibrator 409 may include intrinsic parameters of the sensors (e.g.,optical distortion, beam angles, etc.) and extrinsic parameters. In somecases, calibrator 409 may be performed by maximizing a correlationbetween depth discontinuities in 3D laser data and edges of image data,as an example. Off-line object classification 410 is configured toreceive data, such as sensor data, from sensors 470 or any othercomponent of autonomous vehicle controller 447. According to someembodiments, an off-line classification pipeline of off-line objectclassification 410 may be configured to pre-collect and annotate objects(e.g., manually by a human and/or automatically using an offlinelabeling algorithm), and may further be configured to train an onlineclassifier (e.g., object classifier 444), which can provide real-timeclassification of object types during online autonomous operation.

FIG. 5 is an example of a flow diagram to control an autonomous vehicle,according to some embodiments. At 502, flow 500 begins when sensor dataoriginating from sensors of multiple modalities at an autonomous vehicleis received, for example, by an autonomous vehicle controller. One ormore subsets of sensor data may be integrated for generating fused datato improve, for example, estimates. In some examples, a sensor stream ofone or more sensors (e.g., of same or different modalities) may be fusedto form fused sensor data at 504. In some examples, subsets of LIDARsensor data and camera sensor data may be fused at 504 to facilitatelocalization. At 506, data representing objects based on the least twosubsets of sensor data may be derived at a processor. For example, dataidentifying static objects or dynamic objects may be derived (e.g., at aperception engine) from at least LIDAR and camera data. At 508, adetected object is determined to affect a planned path, and a subset oftrajectories are evaluated (e.g., at a planner) responsive to thedetected object at 510. A confidence level is determined at 512 toexceed a range of acceptable confidence levels associated with normativeoperation of an autonomous vehicle. Therefore, in this case, aconfidence level may be such that a certainty of selecting an optimizedpath is less likely, whereby an optimized path may be determined as afunction of the probability of facilitating collision-free travel,complying with traffic laws, providing a comfortable user experience(e.g., comfortable ride), and/or generating candidate trajectories onany other factor. As such, a request for an alternate path may betransmitted to a teleoperator computing device at 514. Thereafter, theteleoperator computing device may provide a planner with an optimaltrajectory over which an autonomous vehicle made travel. In situations,the vehicle may also determine that executing a safe-stop maneuver isthe best course of action (e.g., safely and automatically causing anautonomous vehicle to a stop at a location of relatively lowprobabilities of danger). Note that the order depicted in this and otherflow charts herein are not intended to imply a requirement to linearlyperform various functions as each portion of a flow chart may beperformed serially or in parallel with anyone or more other portions ofthe flow chart, as well as independent or dependent on other portions ofthe flow chart.

FIG. 6 is a diagram depicting an example of an architecture for anautonomous vehicle controller, according to some embodiments. Diagram600 depicts a number of processes including a motion controller process662, a planner processor 664, a perception process 666, a mappingprocess 640, and a localization process 668, some of which may generateor receive data relative to other processes. Other processes, such assuch as processes 670 and 650 may facilitate interactions with one ormore mechanical components of an autonomous vehicle. For example,perception process 666, mapping process 640, and localization process668 are configured to receive sensor data from sensors 670, whereasplanner process 664 and perception process 666 are configured to receiveguidance data 606, which may include route data, such as road networkdata. Further to diagram 600, localization process 668 is configured toreceive map data 605 a (i.e., 2D map data), map data 605 b (i.e., 3D mapdata), and local map data 642, among other types of map data. Forexample, localization process 668 may also receive other forms of mapdata, such as 4D map data, which may include, for example, an epochdetermination. Localization process 668 is configured to generate localposition data 641 representing a local pose. Local position data 641 isprovided to motion controller process 662, planner process 664, andperception process 666. Perception process 666 is configured to generatestatic and dynamic object map data 667, which, in turn, may betransmitted to planner process 664. In some examples, static and dynamicobject map data 667 may be transmitted with other data, such as semanticclassification information and predicted object behavior. Plannerprocess 664 is configured to generate trajectories data 665, whichdescribes a number of trajectories generated by planner 664. Motioncontroller process uses trajectories data 665 to generate low-levelcommands or control signals for application to actuators 650 to causechanges in steering angles and/or velocity.

FIG. 7 is a diagram depicting an example of an autonomous vehicleservice platform implementing redundant communication channels tomaintain reliable communications with a fleet of autonomous vehicles,according to some embodiments. Diagram 700 depicts an autonomous vehicleservice platform 701 including a reference data generator 705, a vehicledata controller 702, an autonomous vehicle fleet manager 703, ateleoperator manager 707, a simulator 740, and a policy manager 742.Reference data generator 705 is configured to generate and modify mapdata and route data (e.g., RNDF data). Further, reference data generator705 may be configured to access 2D maps in 2D map data repository 720,access 3D maps in 3D map data repository 722, and access route data inroute data repository 724. Other map representation data andrepositories may be implemented in some examples, such as 4D map dataincluding Epoch Determination. Vehicle data controller 702 may beconfigured to perform a variety of operations. For example, vehicle datacontroller 702 may be configured to change a rate that data is exchangedbetween a fleet of autonomous vehicles and platform 701 based on qualitylevels of communication over channels 770. During bandwidth-constrainedperiods, for example, data communications may be prioritized such thatteleoperation requests from autonomous vehicle 730 are prioritizedhighly to ensure delivery. Further, variable levels of data abstractionmay be transmitted per vehicle over channels 770, depending on bandwidthavailable for a particular channel. For example, in the presence of arobust network connection, full LIDAR data (e.g., substantially allLIDAR data, but also may be less) may be transmitted, whereas in thepresence of a degraded or low-speed connection, simpler or more abstractdepictions of the data may be transmitted (e.g., bounding boxes withassociated metadata, etc.). Autonomous vehicle fleet manager 703 isconfigured to coordinate the dispatching of autonomous vehicles 730 tooptimize multiple variables, including an efficient use of batterypower, times of travel, whether or not an air-conditioning unit in anautonomous vehicle 730 may be used during low charge states of abattery, etc., any or all of which may be monitored in view ofoptimizing cost functions associated with operating an autonomousvehicle service. An algorithm may be implemented to analyze a variety ofvariables with which to minimize costs or times of travel for a fleet ofautonomous vehicles. Further, autonomous vehicle fleet manager 703maintains an inventory of autonomous vehicles as well as parts foraccommodating a service schedule in view of maximizing up-time of thefleet.

Teleoperator manager 707 is configured to manage a number ofteleoperator computing devices 704 with which teleoperators 708 provideinput. Simulator 740 is configured to simulate operation of one or moreautonomous vehicles 730, as well as the interactions betweenteleoperator manager 707 and an autonomous vehicle 730. Simulator 740may also simulate operation of a number of sensors (including theintroduction of simulated noise) disposed in autonomous vehicle 730.Further, an environment, such as a city, may be simulated such that asimulated autonomous vehicle can be introduced to the syntheticenvironment, whereby simulated sensors may receive simulated sensordata, such as simulated laser returns. Simulator 740 may provide otherfunctions as well, including validating software updates and/or 2D mapdata. Policy manager 742 is configured to maintain data representingpolicies or rules by which an autonomous vehicle ought to behave in viewof a variety of conditions or events that an autonomous vehicleencounters while traveling in a network of roadways. In some cases,updated policies and/or rules may be simulated in simulator 740 toconfirm safe operation of a fleet of autonomous vehicles in view ofchanges to a policy. Some of the above-described elements of autonomousvehicle service platform 701 are further described hereinafter.

Communication channels 770 are configured to provide networkedcommunication links among a fleet of autonomous vehicles 730 andautonomous vehicle service platform 701. For example, communicationchannel 770 includes a number of different types of networks 771, 772,773, and 774, with corresponding subnetworks (e.g., 771 a to 771 n), toensure a certain level of redundancy for operating an autonomous vehicleservice reliably. For example, the different types of networks incommunication channels 770 may include different cellular networkproviders, different types of data networks, etc., to ensure sufficientbandwidth in the event of reduced or lost communications due to outagesin one or more networks 771, 772, 773, and 774.

FIG. 8 is a diagram depicting an example of a messaging applicationconfigured to exchange data among various applications, according tosome embodiments. Diagram 800 depicts an teleoperator application 801disposed in a teleoperator manager, and an autonomous vehicleapplication 830 disposed in an autonomous vehicle, whereby teleoperatorapplications 801 and autonomous vehicle application 830 exchange messagedata via a protocol that facilitates communications over a variety ofnetworks, such as network 871, 872, and other networks 873. According tosome examples, the communication protocol is a middleware protocolimplemented as a Data Distribution Service™ having a specificationmaintained by the Object Management Group consortium. In accordance withthe communications protocol, teleoperator application 801 and autonomousvehicle application 830 may include a message router 854 disposed in amessage domain, the message router being configured to interface withthe teleoperator API 852. In some examples, message router 854 is arouting service. In some examples, message domain 850 a in teleoperatorapplication 801 may be identified by a teleoperator identifier, whereasmessage domain 850 b be may be identified as a domain associated with avehicle identifier. Teleoperator API 852 in teleoperator application 801is configured to interface with teleoperator processes 803 a to 803 c,whereby teleoperator process 803 b is associated with an autonomousvehicle identifier 804, and teleoperator process 803 c is associatedwith an event identifier 806 (e.g., an identifier that specifies anintersection that may be problematic for collision-free path planning).Teleoperator API 852 in autonomous vehicle application 830 is configuredto interface with an autonomous vehicle operating system 840, whichincludes sensing application 842, a perception application 844, alocalization application 846, and a control application 848. In view ofthe foregoing, the above-described communications protocol mayfacilitate data exchanges to facilitate teleoperations as describedherein. Further, the above-described communications protocol may beadapted to provide secure data exchanges among one or more autonomousvehicles and one or more autonomous vehicle service platforms. Forexample, message routers 854 may be configured to encrypt and decryptmessages to provide for secured interactions between, for example, ateleoperator process 803 and an autonomous vehicle operation system 840.

FIG. 9 is a diagram depicting types of data for facilitatingteleoperations using a communications protocol described in FIG. 8,according to some examples. Diagram 900 depicts a teleoperator 908interfacing with a teleoperator computing device 904 coupled to ateleoperator application 901, which is configured to exchange data via adata-centric messaging bus 972 implemented in one or more networks 971.Data-centric messaging bus 972 provides a communication link betweenteleoperator application 901 and autonomous vehicle application 930.Teleoperator API 962 of teleoperator application 901 is configured toreceive message service configuration data 964 and route data 960, suchas road network data (e.g., RNDF-like data), mission data (e.g.,MDFdata), and the like. Similarly, a messaging service bridge 932 isalso configured to receive messaging service configuration data 934.Messaging service configuration data 934 and 964 provide configurationdata to configure the messaging service between teleoperator application901 and autonomous vehicle application 930. An example of messagingservice configuration data 934 and 964 includes quality of service(“QoS”) configuration data implemented to configure a Data DistributionService™ application.

An example of a data exchange for facilitating teleoperations via thecommunications protocol is described as follows. Consider that obstacledata 920 is generated by a perception system of an autonomous vehiclecontroller. Further, planner options data 924 is generated by a plannerto notify a teleoperator of a subset of candidate trajectories, andposition data 926 is generated by the localizer. Obstacle data 920,planner options data 924, and position data 926 are transmitted to amessaging service bridge 932, which, in accordance with message serviceconfiguration data 934, generates telemetry data 940 and query data 942,both of which are transmitted via data-centric messaging bus 972 intoteleoperator application 901 as telemetry data 950 and query data 952.Teleoperator API 962 receives telemetry data 950 and inquiry data 952,which, in turn are processed in view of Route data 960 and messageservice configuration data 964. The resultant data is subsequentlypresented to a teleoperator 908 via teleoperator computing device 904and/or a collaborative display (e.g., a dashboard display visible to agroup of collaborating teleoperators 908). Teleoperator 908 reviews thecandidate trajectory options that are presented on the display ofteleoperator computing device 904, and selects a guided trajectory,which generates command data 982 and query response data 980, both ofwhich are passed through teleoperator API 962 as query response data 954and command data 956. In turn, query response data 954 and command data956 are transmitted via data-centric messaging bus 972 into autonomousvehicle application 930 as query response data 944 and command data 946.Messaging service bridge 932 receives query response data 944 andcommand data 946 and generates teleoperator command data 928, which isconfigured to generate a teleoperator-selected trajectory forimplementation by a planner. Note that the above-described messagingprocesses are not intended to be limiting, and other messaging protocolsmay be implemented as well.

FIG. 10 is a diagram illustrating an example of a teleoperator interfacewith which a teleoperator may influence path planning, according to someembodiments. Diagram 1000 depicts examples of an autonomous vehicle 1030in communication with an autonomous vehicle service platform 1001, whichincludes a teleoperator manager 1007 configured to facilitateteleoperations. In a first example, teleoperator manager 1007 receivesdata that requires teleoperator 1008 to preemptively view a path of anautonomous vehicle approaching a potential obstacle or an area of lowplanner confidence levels so that teleoperator 1008 may be able toaddress an issue in advance. To illustrate, consider that anintersection that an autonomous vehicle is approaching may be tagged asbeing problematic. As such, user interface 1010 displays arepresentation 1014 of a corresponding autonomous vehicle 1030transiting along a path 1012, which has been predicted by a number oftrajectories generated by a planner. Also displayed are other vehicles1011 and dynamic objects 1013, such as pedestrians, that may causesufficient confusion at the planner, thereby requiring teleoperationsupport. User interface 1010 also presents to teleoperator 1008 acurrent velocity 1022, a speed limit 1024, and an amount of charge 1026presently in the batteries. According to some examples, user interface1010 may display other data, such as sensor data as acquired fromautonomous vehicle 1030. In a second example, consider that planner 1064has generated a number of trajectories that are coextensive with aplanner-generated path 1044 regardless of a detected unidentified object1046. Planner 1064 may also generate a subset of candidate trajectories1040, but in this example, the planner is unable to proceed givenpresent confidence levels. If planner 1064 fails to determine analternative path, a teleoperation request may be transmitted. In thiscase, a teleoperator may select one of candidate trajectories 1040 tofacilitate travel by autonomous vehicle 1030 that is consistent withteleoperator-based path 1042.

FIG. 11 is a diagram depicting an example of a planner configured toinvoke teleoperations, according to some examples. Diagram 1100 depictsa planner 1164 including a topography manager 1110, a route manager1112, a path generator 1114, a trajectory evaluator 1120, and atrajectory tracker 1128. Topography manager 1110 is configured toreceive map data, such as 3D map data or other like map data thatspecifies topographic features. Topography manager 1110 is furtherconfigured to identify candidate paths based on topographic-relatedfeatures on a path to a destination. According to various examples,topography manager 1110 receives 3D maps generated by sensors associatedwith one or more autonomous vehicles in the fleet. Route manager 1112 isconfigured to receive environmental data 1103, which may includetraffic-related information associated with one or more routes that maybe selected as a path to the destination. Path generator 1114 receivesdata from topography manager 1110 and route manager 1112, and generatesone or more paths or path segments suitable to direct autonomous vehicletoward a destination. Data representing one or more paths or pathsegments is transmitted into trajectory evaluator 1120.

Trajectory evaluator 1120 includes a state and event manager 1122,which, in turn, may include a confidence level generator 1123.Trajectory evaluator 1120 further includes a guided trajectory generator1126 and a trajectory generator 1124. Further, planner 1164 isconfigured to receive policy data 1130, perception engine data 30 1132,and localizer data 1134.

Policy data 1130 may include criteria with which planner 1164 uses todetermine a path that has a sufficient confidence level with which togenerate trajectories, according to some examples. Examples of policydata 1130 include policies that specify that trajectory generation isbounded by stand-off distances to external objects (e.g., maintaining asafety buffer of 3 feet from a cyclist, as possible), or policies thatrequire that trajectories must not cross a center double yellow line, orpolicies that require trajectories to be limited to a single lane in a4-lane roadway (e.g., based on past events, such as typicallycongregating at a lane closest to a bus stop), and any other similarcriteria specified by policies. Perception engine data 1132 includesmaps of locations of static objects and dynamic objects of interest, andlocalizer data 1134 includes at least a local pose or position.

State and event manager 1122 may be configured to probabilisticallydetermine a state of operation for an autonomous vehicle. For example, afirst state of operation (i.e., “normative operation”) may describe asituation in which trajectories are collision-free, whereas a secondstate of operation (i.e., “non-normative operation”) may describeanother situation in which the confidence level associated with possibletrajectories are insufficient to guarantee collision-free travel.According to some examples, state and event manager 1122 is configuredto use perception data 1132 to determine a state of autonomous vehiclethat is either normative or non-normative. Confidence level generator1123 may be configured to analyze perception data 1132 to determine astate for the autonomous vehicle. For example, confidence levelgenerator 1123 may use semantic information associated with static anddynamic objects, as well as associated probabilistic estimations, toenhance a degree of certainty that planner 1164 is determining safecourse of action. For example, planner 1164 may use perception enginedata 1132 that specifies a probability that an object is either a personor not a person to determine whether planner 1164 is operating safely(e.g., planner 1164 may receive a degree of certainty that an object hasa 98% probability of being a person, and a probability of 2% that theobject is not a person).

Upon determining a confidence level (e.g., based on statistics and 30probabilistic determinations) is below a threshold required forpredicted safe operation, relatively low confidence level (e.g., singleprobability score) may trigger planner 1164 to transmit a request 1135for teleoperation support to autonomous vehicle service platform 1101.In some cases, telemetry data and a set of candidate trajectories mayaccompany the request. Examples of telemetry data include sensor data,localization data, perception data, and the like. A teleoperator 1108may transmit via teleoperator computing device 1104 a selectedtrajectory 1137 to guided trajectory generator 1126. As such, selectedtrajectory 1137 is a trajectory formed with guidance from ateleoperator. Upon confirming there is no change in the state (e.g., anon-normative state is pending), guided trajectory generator 1126 passesdata to trajectory generator 1124, which, in turn, causes trajectorytracker 1128, as a trajectory tracking controller, to use theteleop-specified trajectory for generating control signals 1170 (e.g.,steering angles, velocity, etc.). Note that planner 1164 may triggertransmission of a request 1135 for teleoperation support prior to astate transitioning to a non-normative state. In particular, anautonomous vehicle controller and/or its components can predict that adistant obstacle may be problematic and preemptively cause planner 1164to invoke teleoperations prior to the autonomous vehicle reaching theobstacle. Otherwise, the autonomous vehicle may cause a delay bytransitioning to a safe state upon encountering the obstacle or scenario(e.g., pulling over and off the roadway). In another example,teleoperations may be automatically invoked prior to an autonomousvehicle approaching a particular location that is known to be difficultto navigate. This determination may optionally take into considerationother factors, including the time of day, the position of the sun, ifsuch situation is likely to cause a disturbance to the reliability ofsensor readings, and traffic or accident data derived from a variety ofsources.

FIG. 12 is an example of a flow diagram configured to control anautonomous vehicle, according to some embodiments. At 1202, flow 1200begins. Data representing a subset of objects that are received at aplanner in an autonomous vehicle, the subset of objects including atleast one object associated with data representing a degree of certaintyfor a classification type. For example, perception engine data mayinclude metadata associated with objects, whereby the metadata specifiesa degree of certainty associated with a specific classification type.For instance, a dynamic object may be classified as a “young pedestrian”with an 85% confidence level of being correct. At 1204, localizer datamay be received (e.g., at a planner). The localizer data may include mapdata that is generated locally within the autonomous vehicle. The localmap data may specify a degree of certainty (including a degree ofuncertainty) that an event at a geographic region may occur. An eventmay be a condition or situation affecting operation, or potentiallyaffecting operation, of an autonomous vehicle. The events may beinternal (e.g., failed or impaired sensor) to an autonomous vehicle, orexternal (e.g., roadway obstruction). Examples of events are describedherein, such as in FIG. 2 as well as in other figures and passages. Apath coextensive with the geographic region of interest may bedetermined at 1206. For example, consider that the event is thepositioning of the sun in the sky at a time of day in which theintensity of sunlight impairs the vision of drivers during rush hourtraffic. As such, it is expected or predicted that traffic may slow downresponsive to the bright sunlight. Accordingly, a planner maypreemptively invoke teleoperations if an alternate path to avoid theevent is less likely. At 1208, a local position is determined at aplanner based on local pose data. At 1210, a state of operation of anautonomous vehicle may be determined (e.g., probabilistically), forexample, based on a degree of certainty for a classification type and adegree of certainty of the event, which is may be based on any number offactors, such as speed, position, and other state information. Toillustrate, consider an example in which a young pedestrian is detectedby the autonomous vehicle during the event in which other drivers'vision likely will be impaired by the sun, thereby causing an unsafesituation for the young pedestrian. Therefore, a relatively unsafesituation can be detected as a probabilistic event that may be likely tooccur (i.e., an unsafe situation for which teleoperations may beinvoked). At 1212, a likelihood that the state of operation is in anormative state is determined, and based on the determination, a messageis transmitted to a teleoperator computing device requestingteleoperations to preempt a transition to a next state of operation(e.g., preempt transition from a normative to non-normative state ofoperation, such as an unsafe state of operation).

FIG. 13 depicts an example in which a planner may generate a 30trajectory, according to some examples. Diagram 1300 includes atrajectory evaluator 1320 and a trajectory generator 1324. Trajectoryevaluator 1320 includes a confidence level generator 1322 and ateleoperator query messenger 1329. As shown, trajectory evaluator 1320is coupled to a perception engine 1366 to receive static map data 1301,and current and predicted object state data 1303. Trajectory evaluator1320 also receives local pose data 1305 from localizer 1368 and plandata 1307 from a global planner 1369. In one state of operation (e.g.,non-normative), confidence level generator 1322 receives static map data1301 and current and predicted object state data 1303. Based on thisdata, confidence level generator 1322 may determine that detectedtrajectories are associated with unacceptable confidence level values.As such, confidence level generator 1322 transmits detected trajectorydata 1309 (e.g., data including candidate trajectories) to notify ateleoperator via teleoperator query messenger 1329, which, in turn,transmits a request 1370 for teleoperator assistance.

In another state of operation (e.g., a normative state), static map data1301, current and predicted object state data 1303, local pose data1305, and plan data 1307 (e.g., global plan data) are received intotrajectory calculator 1325, which is configured to calculate (e.g.,iteratively) trajectories to determine an optimal one or more paths.Next, at least one path is selected and is transmitted as selected pathdata 1311. According to some embodiments, trajectory calculator 1325 isconfigured to implement re-planning of trajectories as an example.Nominal driving trajectory generator 1327 is configured to generatetrajectories in a refined approach, such as by generating trajectoriesbased on receding horizon control techniques. Nominal driving trajectorygenerator 1327 subsequently may transmit nominal driving trajectory pathdata 1372 to, for example, a trajectory tracker or a vehicle controllerto implement physical changes in steering, acceleration, and othercomponents.

FIG. 14 is a diagram depicting another example of an autonomous vehicleservice platform, according to some embodiments. Diagram 1400 depicts anautonomous vehicle service platform 1401 including a teleoperatormanager 1407 that is configured to manage interactions and/orcommunications among teleoperators 1408, teleoperator computing devices1404, and other components of autonomous vehicle service platform 1401.Further to diagram 1400, autonomous vehicle service platform 1401includes a simulator 1440, a repository 1441, a policy manager 1442, areference data updater 1438, a 20 map data repository 1420, a 3D mapdata repository 1422, and a route data repository 1424. Other map data,such as 40 map data (e.g., using epoch determination), may beimplemented and stored in a repository (not shown).

Teleoperator action recommendation controller 1412 includes logicconfigured to receive and/or control a teleoperation service request viaautonomous vehicle (“AV”) planner data 1472, which can include requestsfor teleoperator assistance as well as telemetry data and other data. Assuch, planner data 1472 may include recommended candidate trajectoriesor paths from which a teleoperator 1408 via teleoperator computingdevice 1404 may select. According to some examples, teleoperator actionrecommendation controller 1412 may be configured to access other sourcesof recommended candidate trajectories from which to select an optimumtrajectory. For example, candidate trajectories contained in autonomousvehicle planner data 1472 may, in parallel, be introduced into simulator1440, which is configured to simulate an event or condition beingexperienced by an autonomous vehicle requesting teleoperator assistance.Simulator 1440 can access map data and other data necessary forperforming a simulation on the set of candidate trajectories, wherebysimulator 1440 need not exhaustively reiterate simulations to confirmsufficiency. Rather, simulator 1440 may provide either confirm theappropriateness of the candidate trajectories, or may otherwise alert ateleoperator to be cautious in their selection.

Teleoperator interaction capture analyzer 1416 may be configured tocapture numerous amounts of teleoperator transactions or interactionsfor storage in repository 1441, which, for example, may accumulate datarelating to a number of teleoperator transactions for analysis andgeneration of policies, at least in some cases. According to someembodiments, repository 1441 may also be configured to store policy datafor access by policy manager 1442. Further, teleoperator interactioncapture analyzer 1416 may apply machine learning techniques toempirically determine how best to respond to events or conditionscausing requests for teleoperation assistance. In some cases, policymanager 1442 may be configured to update a particular policy or generatea new policy responsive to analyzing the large set of teleoperatorinteractions (e.g., subsequent to applying machine learning techniques).Policy manager 1442 manages policies that may be viewed as rules orguidelines with which an autonomous vehicle controller and itscomponents operate under to comply with autonomous operations of avehicle. In some cases, a modified or updated policy may be applied tosimulator 1440 to confirm the efficacy of permanently releasing orimplementing such policy changes.

Simulator interface controller 1414 is configured to provide aninterface between simulator 1440 and teleoperator computing devices1404. For example, consider that sensor data from a fleet of autonomousvehicles is applied to reference data updater 1438 via autonomous (“AV”)fleet data 1470, whereby reference data updater 1438 is configured togenerate updated map and route data 1439. In some implementations,updated map and route data 1439 may be preliminarily released as anupdate to data in map data repositories 1420 and 1422, or as an updateto data in route data repository 1424. In this case, such data may betagged as being a “beta version” in which a lower threshold forrequesting teleoperator service may be implemented when, for example, amap tile including preliminarily updated information is used by anautonomous vehicle. Further, updated map and route data 1439 may beintroduced to simulator 1440 for validating the updated map data. Uponfull release (e.g., at the close of beta testing), the previouslylowered threshold for requesting a teleoperator service related to maptiles is canceled. User interface graphics controller 1410 provides richgraphics to teleoperators 1408, whereby a fleet of autonomous vehiclesmay be simulated within simulator 1440 and may be accessed viateleoperator computing device 1404 as if the simulated fleet ofautonomous vehicles were real.

FIG. 15 is an example of a flow diagram to control an autonomousvehicle, according to some embodiments. At 1502, flow 1500 begins.Message data may be received at a teleoperator computing device formanaging a fleet of autonomous vehicles. The message data may indicateevent attributes associated with a non-normative state of operation inthe context of a planned path for an autonomous vehicle. For example, anevent may be characterized as a particular intersection that becomesproblematic due to, for example, a large number of pedestrians,hurriedly crossing the street against a traffic light. The eventattributes describe the characteristics of the event, such as, forexample, the number of people crossing the street, the traffic delaysresulting from an increased number of pedestrians, etc. At 1504, ateleoperation repository may be accessed to retrieve a first subset ofrecommendations based on simulated operations of aggregated dataassociated with a group of autonomous vehicles. In this case, asimulator may be a source of recommendations with which a teleoperatormay implement. Further, the teleoperation repository may also beaccessed to retrieve a second subset of recommendations based on anaggregation of teleoperator interactions responsive to similar eventattributes. In particular, a teleoperator interaction capture analyzermay apply machine learning techniques to empirically determine how bestto respond to events having similar attributes based on previousrequests for teleoperation assistance. At 1506, the first subset and thesecond subset of recommendations are combined to form a set ofrecommended courses of action for the autonomous vehicle. At 1508,representations of the set of recommended courses of actions may bepresented visually on a display of a teleoperator computing device. At1510, data signals representing a selection (e.g., by teleoperator) of arecommended course of action may be detected.

FIG. 16 is a diagram of an example of an autonomous vehicle fleetmanager implementing a fleet optimization manager, according to someexamples. Diagram 1600 depicts an autonomous vehicle fleet manager thatis configured to manage a fleet of autonomous vehicles 1630 transitingwithin a road network 1650. Autonomous vehicle fleet manager 1603 iscoupled to a teleoperator 1608 via a teleoperator computing device 1604,and is also coupled to a fleet management data repository 1646.Autonomous vehicle fleet manager 1603 is configured to receive policydata 1602 and environmental data 1606, as well as other data. Further todiagram 1600, fleet optimization manager 1620 is shown to include atransit request processor 1631, which, in turn, includes a fleet dataextractor 1632 and an autonomous vehicle dispatch optimizationcalculator 1634. Transit request processor 1631 is configured to processtransit requests, such as from a user 1688 who is requesting autonomousvehicle service. Fleet data extractor 1632 is configured to extract datarelating to autonomous vehicles in the fleet. Data associated with eachautonomous vehicle is stored in repository 1646. For example, data foreach vehicle may describe maintenance issues, scheduled service calls,daily usage, battery charge and discharge rates, and any other data,which may be updated in real-time, may be used for purposes ofoptimizing a fleet of autonomous vehicles to minimize downtime.Autonomous vehicle dispatch optimization calculator 1634 is configuredto analyze the extracted data and calculate optimized usage of the fleetso as to ensure that the next vehicle dispatched, such as from station1652, provides for the least travel times and/or costs-in theaggregate-for the autonomous vehicle service.

Fleet optimization manager 1620 is shown to include a hybrid autonomousvehicle/non-autonomous vehicle processor 1640, which, in turn, includesan AV/non-AV optimization calculator 1642 and a non-AV selector 1644.According to some examples, hybrid autonomous vehicle/non-autonomousvehicle processor 1640 is configured to manage a hybrid fleet ofautonomous vehicles and human-driven vehicles (e.g., as independentcontractors). As such, autonomous vehicle service may employnon-autonomous vehicles to meet excess demand, or in areas, such asnon-AV service region 1690, that may be beyond a geo-fence or in areasof poor communication coverage. AV/non-AV optimization calculator 1642is configured to optimize usage of the fleet of autonomous and to invitenon-AV drivers into the transportation service (e.g., with minimal or nodetriment to the autonomous vehicle service). Non-AV selector 1644includes logic for selecting a number of non-AV drivers to assist basedon calculations derived by AV/non-AV optimization calculator 1642.

FIG. 17 is an example of a flow diagram to manage a fleet of autonomousvehicles, according to some embodiments. At 1702, flow 1700 begins. At1702, policy data is received. The policy data may include parametersthat define how best apply to select an autonomous vehicle for servicinga transit request. At 1704, fleet management data from a repository maybe extracted. The fleet management data includes subsets of data for apool of autonomous vehicles (e.g., the data describes the readiness ofvehicles to service a transportation request). At 1706, datarepresenting a transit request is received. For exemplary purposes, thetransit request could be for transportation from a first geographiclocation to a second geographic location. At 1708, attributes based onthe policy data are calculated to determine a subset of autonomousvehicles that are available to service the request. For example,attributes may include a battery charge level and time until nextscheduled maintenance. At 1710, an autonomous vehicle is selected astransportation from the first geographic location to the secondgeographic location, and data is generated to dispatch the autonomousvehicle to a third geographic location associated with the originationof the transit request.

FIG. 18 is a diagram illustrating an autonomous vehicle fleet managerimplementing an autonomous vehicle communications link manager,according to some embodiments. Diagram 1800 depicts an autonomousvehicle fleet manager that is configured to manage a fleet of autonomousvehicles 1830 transiting within a road network 1850 that coincides witha communication outage at an area identified as “reduced communicationregion” 1880. Autonomous vehicle fleet manager 1803 is coupled to ateleoperator 1808 via a teleoperator computing device 1804. Autonomousvehicle fleet manager 1803 is configured to receive polity data 1802 andenvironmental data 1806, as well as other data. Further to diagram 1800,an autonomous vehicle communications link manager 1820 is shown toinclude an environment event detector 1831, a policy adaptiondeterminator 1832, and a transit request processor 1834. Environmentevent detector 1831 is configured to receive environmental data 1806specifying a change within the environment in which autonomous vehicleservice is implemented. For example, environmental data 1806 may specifythat region 1880 has degraded communication services, which may affectthe autonomous vehicle service. Policy adaption determinator 1832 mayspecify parameters with which to apply when receiving transit requestsduring such an event (e.g., during a loss of communications). Transitrequest processor 1834 is configured to process transit requests in viewof the degraded communications. In this example, a user 1888 isrequesting autonomous vehicle service. Further, transit requestprocessor 1834 includes logic to apply an adapted policy for modifyingthe way autonomous vehicles are dispatched so to avoid complications dueto poor communications.

Communication event detector 1840 includes a policy download manager1842 and communications-configured (“COMM-configured”) AV dispatcher1844. Policy download manager 1842 is configured to provide autonomousvehicles 1830 an updated policy in view of reduced communications region1880, whereby the updated policy may specify routes to quickly exitregion 1880 if an autonomous vehicle enters that region. For example,autonomous vehicle 1864 may receive an updated policy moments beforedriving into region 1880. Upon loss of communications, autonomousvehicle 1864 implements the updated policy and selects route 1866 todrive out of region 1880 quickly. COMM-configured AV dispatcher 1844 maybe configured to identify points 1865 at which to park autonomousvehicles that are configured as relays to establishing a peer-to-peernetwork over region 1880. As such, COMM-configured AV dispatcher 1844 isconfigured to dispatch autonomous vehicles 1862 (without passengers) topark at locations 1865 for the purposes of operating as communicationtowers in a peer-to-peer ad hoc network.

FIG. 19 is an example of a flow diagram to determine actions forautonomous vehicles during an event, such as degraded or lostcommunications, according to some embodiments. At 1901, flow 1900begins. Policy data is received, whereby the policy data definesparameters with which to apply to transit requests in a geographicalregion during an event. At 1902, one or more of the following actionsmay be implemented: (1) dispatch a subset of autonomous vehicles togeographic locations in the portion of the geographic location, thesubset of autonomous vehicles being configured to either park atspecific geographic locations and each serve as a static communicationrelay, or transit in a geographic region to each serve as a mobilecommunication relay, (2) implement peer-to-peer communications among aportion of the pool of autonomous vehicles associated with the portionof the geographic region, (3) provide to the autonomous vehicles anevent policy that describes a route to egress the portion of thegeographic region during an event, (4) invoke teleoperations, and (5)recalculate paths so as to avoid the geographic portion. Subsequent toimplementing the action, the fleet of autonomous vehicles is monitoredat 1914.

FIG. 20 is a diagram depicting an example of a localizer, according tosome embodiments. Diagram 2000 includes a localizer 2068 configured toreceive sensor data from sensors 2070, such as LIDAR data 2072, cameradata 2074, radar data 2076, and other data 2078. Further, localizer 2068is configured to receive reference data 3D 2020, such as 2D map data2022, 3D map data 2024, and 3D local map data. According to someexamples, other map data, such as 4D map data 2025 and semantic map data(not shown), including corresponding data structures and repositories,may also be implemented. Further to diagram 2000, localizer 2068includes a positioning system 2010 and a localization system 2012, bothof which are configured to receive sensor data from sensors 2070 as wellas reference data 2020. Localization data integrator 2014 is configuredto receive data from positioning system 2010 and data from localizationsystem 2012, whereby localization data integrator 2014 is configured tointegrate or fuse sensor data from multiple sensors to form local posedata 2052.

FIG. 21 is an example of a flow diagram to generate local pose databased on integrated sensor data, according to some embodiments. At 2101,flow 2100 begins. At 2102, reference data is received, the referencedata including three dimensional map data. In some examples, referencedata, such as 3D or 4D map data, may be received via one or morenetworks. At 2104, localization data from one or more localizationsensors is received and placed into a localization system. At 2106,positioning data from one or more positioning sensors is received into apositioning system. At 2108, the localization and positioning data areintegrated. At 2110, the localization data and positioning data areintegrated to form local position data specifying a geographic positionof an autonomous vehicle.

FIG. 22 is a diagram depicting another example of a localizer, accordingto some embodiments. Diagram 2200 includes a localizer 2268, which, inturn, includes a localization system 2210 and a relative localizationsystem 2212 to generate positioning-based data 2250 and locallocation-based data 2251, respectively. Localization system 2210includes a projection processor 2254 a for processing GPS data 2273, aGPS datum 2211, and 3D Map data 2222, among other optional data (e.g.,4D map data). Localization system 2210 also includes an odometryprocessor 2254 b to process wheel data 2275 (e.g., wheel speed), vehiclemodel data 2213 and 3D map data 2222, among other optional data. Furtheryet, localization system 2210 includes an integrator processor 2254 c toprocess IMU data 2257, vehicle model data 2215, and 3D map data 2222,among other optional data. Similarly, relative localization system 221230 includes a LIDAR localization processor 2254 d for processing LIDARdata 2272, 2D tile map data 2220, 3D map data 2222, and 3D local mapdata 2223, among other optional data. Relative localization system 2212also includes a visual registration processor 2254 e to process cameradata 2274, 3D map data 2222, and 3D local map data 2223, among otheroptional data. Further yet, relative localization system 2212 includes aradar return processor 2254 f to process radar data 2276, 3D map data2222, and 3D local map data 2223, among other optional data. Note thatin various examples, other types of sensor data and sensors orprocessors may be implemented, such as sonar data and the like.

Further to diagram 2200, localization-based data 2250 and relativelocalization-based data 2251 may be fed into data integrator 2266 a andlocalization data integrator 2266, respectively. Data integrator 2266 aand localization data integrator 2266 may be configured to fusecorresponding data, whereby localization-based data 2250 may be fused atdata integrator 2266 a prior to being fused with relativelocalization-based data 2251 at localization data integrator 2266.According to some embodiments, data integrator 2266 a is formed as partof localization data integrator 2266, or is absent. Regardless, alocalization-based data 2250 and relative localization-based data 2251can be both fed into localization data integrator 2266 for purposes offusing data to generate local position data 2252. Localization-baseddata 2250 may include unary-constrained data (and uncertainty values)from projection processor 2254 a, as well as binary-constrained data(and uncertainty values) from odometry processor 2254 b and integratorprocessor 2254 c. Relative localization-based data 2251 may includeunary-constrained data (and uncertainty values) from localizationprocessor 2254 d and visual registration processor 2254 e, andoptionally from radar return processor 2254 f. According to someembodiments, localization data integrator 2266 may implement non-linearsmoothing functionality, such as a Kalman filter (e.g., a gated Kalmanfilter), a relative bundle adjuster, pose-graph relaxation, particlefilter, histogram filter, or the like.

FIG. 23 is a diagram depicting an example of a perception engine,according to some embodiments. Diagram 2300 includes a perception engine2366, which, in turn, includes a segmentation processor 2310, an objecttracker 2330, and a classifier 2360. Further, perception engine 2366 isconfigured to receive a local position data 2352, LIDAR data 2372,camera data 2374, and radar data 2376, for example that other sensordata, such as sonar data, may be accessed to provide functionalities ofperception engine 2366. Segmentation processor 2310 is configured toextract ground plane data and/or to segment portions of an image todistinguish objects from each other and from static imagery (e.g.,background). In some cases, 3D blobs may be segmented to distinguisheach other. In some examples, a blob may refer to a set of features thatidentify an object in a spatially-reproduced environment and may becomposed of elements (e.g., pixels of camera data, points of laserreturn data, etc.) having similar characteristics, such as intensity andcolor. In some examples, a blob may also refer to a point cloud (e.g.,composed of colored laser return data) or other elements constituting anobject. Object tracker 2330 is configured to perform frame-to-frameestimations of motion for blobs, or other segmented image portions.Further, data association is used to associate a blob at one location ina first frame at time, t1, to a blob in a different position in a secondframe at time, t2. In some examples, object tracker 2330 is configuredto perform real-time probabilistic tracking of 3D objects, such asblobs. Classifier 2360 is configured to identify an object and toclassify that object by classification type (e.g., as a pedestrian,cyclist, etc.) and by energy/activity (e.g. whether the object isdynamic or static), whereby data representing classification isdescribed by a semantic label. According to some embodiments,probabilistic estimations of object categories may be performed, such asclassifying an object as a vehicle, bicyclist, pedestrian, etc. withvarying confidences per object class. Perception engine 2366 isconfigured to determine perception engine data 2354, which may includestatic object maps and/or dynamic object maps, as well as semanticinformation so that, for example, a planner may use this information toenhance path planning. According to various examples, one or more ofsegmentation processor 2310, object tracker 2330, and classifier 2360may apply machine learning techniques to generate perception engine data2354.

FIG. 24 is an example of a flow chart to generate perception enginedata, according to some embodiments. Flow chart 2400 begins at 2402, atwhich data representing a local position of an autonomous vehicle isretrieved. At 2404, localization data from one or more localizationsensors is received, and features of an environment in which theautonomous vehicle is disposed are segmented at 2406 to form segmentedobjects. One or more portions of the segmented object are trackedspatially at 2408 to form at least one tracked object having a motion(e.g., an estimated motion). At 2410, a tracked object is classified atleast as either being a static object or a dynamic object. In somecases, a static object or a dynamic object may be associated with aclassification type. At 2412, data identifying a classified object isgenerated. For example, the data identifying the classified object mayinclude semantic information.

FIG. 25 is an example of a segmentation processor, according to someembodiments. Diagram 2500 depicts a segmentation processor 2510receiving LIDAR data from one or more LIDARs 2572 and camera image datafrom one or more cameras 2574. Local pose data 2552, LIDAR data, andcamera image data are received into meta spin generator 2521. In someexamples, meta spin generator is configured to partition an image basedon various attributes (e.g., color, intensity, etc.) intodistinguishable regions (e.g., clusters or groups of a point cloud), atleast two or more of which may be updated at the same time or about thesame time. Meta spin data 2522 is used to perform object segmentationand ground segmentation at segmentation processor 2523, whereby bothmeta spin data 2522 and segmentation-related data from segmentationprocessor 2523 are applied to a scanned differencing processor 2513.Scanned differencing processor 2513 is configured to predict motionand/or relative velocity of segmented image portions, which can be usedto identify dynamic objects at 2517. Data indicating objects withdetected velocity at 2517 are optionally transmitted to the planner toenhance path planning decisions. Additionally, data from scanneddifferencing processor 2513 may be used to approximate locations ofobjects to form mapping of such objects (as well as optionallyidentifying a level of motion). In some examples, an occupancy grid map2515 may be generated. Data representing an occupancy grid map 2515 maybe transmitted to the planner to further enhance path planning decisions(e.g., by reducing uncertainties). Further to diagram 2500, image cameradata from one or more cameras 2574 are used to classify blobs in blobclassifier 2520, which also receives blob data 2524 from segmentationprocessor 2523. Segmentation processor 2510 also may receive raw radarreturns data 2512 from one or more radars 2576 to perform segmentationat a radar segmentation processor 2514, which generates radar-relatedblob data 2516. Further to FIG. 25, segmentation processor 2510 may alsoreceive and/or generate tracked blob data 2518 related to radar data.Blob data 2516, tracked blob data 2518, data from blob classifier 2520,and blob data 2524 may be used to track objects or portions thereof.According to some examples, one or more of the following may beoptional: scanned differencing processor 2513, blob classification 2520,and data from radar 2576.

FIG. 26A is a diagram depicting examples of an object tracker and aclassifier, according to various embodiments. Object tracker 2630 ofdiagram 2600 is configured to receive blob data 2516, tracked blob data2518, data from blob classifier 2520, blob data 2524, and camera imagedata from one or more cameras 2676. Image tracker 2633 is configured toreceive camera image data from one or more cameras 2676 to generatetracked image data, which, in turn, may be provided to data associationprocessor 2632. As shown, data association processor 2632 is configuredto receive blob data 2516, tracked blob data 2518, data from blobclassifier 2520, blob data 2524, and track image data from image tracker2633, and is further configured to identify one or more associationsamong the above-described types of data. Data association processor 2632is configured to track, for example, various blob data from one frame toa next frame to, for example, estimate motion, among other things.Further, data generated by data association processor 2632 may be usedby track updater 2634 to update one or more tracks, or tracked objects.In some examples, track updater 2634 may implement a Kalman Filter, orthe like, to form updated data for tracked objects, which may be storedonline in track database (“DB”) 2636. Feedback data may be exchanged viapath 2699 between data association processor 2632 and track database2636. In some examples, image tracker 2633 may be optional and may beexcluded. Object tracker 2630 may also use other sensor data, such asradar or sonar, as well as any other types of sensor data, for example.

FIG. 26B is a diagram depicting another example of an object trackeraccording to at least some examples. Diagram 2601 includes an objecttracker 2631 that may include structures and/or functions assimilarly-named elements described in connection to one or more otherdrawings (e.g., FIG. 26A). As shown, object tracker 2631 includes anoptional registration portion 2699 that includes a processor 2696configured to perform object scan registration and data fusion.Processor 2696 is further configured to store the resultant data in 3Dobject database 2698.

Referring back to FIG. 26A, diagram 2600 also includes classifier 2660,which may include a track classification engine 2662 for generatingstatic obstacle data 2672 and dynamic obstacle data 2674, both of whichmay be transmitted to the planner for path planning purposes. In atleast one example, track classification engine 2662 is configured todetermine whether an obstacle is static or dynamic, as well as anotherclassification type for the object (e.g., whether the object is avehicle, pedestrian, tree, cyclist, dog, cat, paper bag, etc.). Staticobstacle data 2672 may be formed as part of an obstacle map (e.g., a 2Doccupancy map), and dynamic obstacle data 2674 may be formed to includebounding boxes with data indicative of velocity and classification type.Dynamic obstacle data 2674, at least in some cases, includes dynamicobstacle map data.

FIG. 27 is an example of front-end processor for a perception engine,according to some examples. Diagram 2700 includes a ground segmentationprocessor 2723 a for performing ground segmentation, and an oversegmentation processor 2723 b for performing “over-segmentation”according to various examples. Processors 2723 a and 2723 b areconfigured to receive optionally colored LIDAR data 2775. Oversegmentation processor 2723 b generates data 2710 of a first blob type(e.g., a relatively small blob), which is provided to an aggregationclassification and segmentation engine 2712 that generates data 2714 ofa second blob type. Data 2714 is provided to data association processor2732, which is configured to detect whether data 2714 resides in trackdatabase 2736. A determination is made at 2740 whether data 2714 of thesecond blob type (e.g., a relatively large blob, which may include oneor more smaller blobs) is a new track. If so, a track is initialized at2742, otherwise, the tracked object data stored in track database 2736and the track may be extended or updated by track updater 2742. Trackclassification engine 2762 is coupled to track database 2736 to identifyand update/modify tracks by, for example, adding, removing or modifyingtrack-related data.

FIG. 28 is a diagram depicting a simulator configured to simulate anautonomous vehicle in a synthetic environment, according to variousembodiments. Diagram 2800 includes a simulator 2840 that is configuredto generate a simulated environment 2803. As shown, simulator 2840 isconfigured to use reference data 2822 (e.g., 3D map data and/or othermap or route data including RNDF data or similar road network data) togenerate simulated geometries, such as simulated surfaces 2892 a and2892 b, within simulated environment 2803. Simulated surfaces 2892 a and2892 b may simulate walls or front sides of buildings adjacent aroadway. Simulator 2840 may also pre-generated or procedurally generateduse dynamic object data 2825 to simulate dynamic agents in a syntheticenvironment. An example of a dynamic agent is simulated dynamic object2801, which is representative of a simulated cyclist having a velocity.The simulated dynamic agents may optionally respond to other static anddynamic agents in the simulated environment, including the simulatedautonomous vehicle. For example, simulated object 2801 may slow down forother obstacles in simulated environment 2803 rather than follow apreset trajectory, thereby creating a more realistic simulation ofactual dynamic environments that exist in the real world.

Simulator 2840 may be configured to generate a simulated autonomousvehicle controller 2847, which includes synthetic adaptations of aperception engine 2866, a localizer 2868, a motion controller 2862, anda planner 2864, each of which may have functionalities described hereinwithin simulated environment 2803. Simulator 2840 may also generatesimulated interfaces (“I/F”) 2849 to simulate the data exchanges withdifferent sensors modalities and different sensor data formats. As such,simulated interface 2849 may simulate a software interface forpacketized data from, for example, a simulated LIDAR sensor 2872.Further, simulator 2840 may also be configured to generate a simulatedautonomous vehicle 2830 that implements simulated AV controller 2847.Simulated autonomous vehicle 2830 includes simulated LIDAR sensors 2872,simulated camera or image sensors 2874, and simulated radar sensors2876. In the example shown, simulated LIDAR sensor 2872 may beconfigured to generate a simulated laser consistent with ray trace 2892,which causes generation of simulated sensor return 2891. Note thatsimulator 2840 may simulate the addition of noise or other environmentaleffects on sensor data (e.g., added diffusion or reflections that affectsimulated sensor return 2891, etc.). Further yet, simulator 2840 may beconfigured to simulate a variety of sensor defects, including sensorfailure, sensor miscalibration, intermittent data outages, and the like.

Simulator 2840 includes a physics processor 2850 for simulating themechanical, static, dynamic, and kinematic aspects of an autonomousvehicle for use in simulating behavior of simulated autonomous vehicle2830. For example, physics processor 2850 includes a content mechanicsmodule 2851 for simulating contact mechanics, a collision detectionmodule 2852 for simulating the interaction between simulated bodies, anda multibody dynamics module 2854 to simulate the interaction betweensimulated mechanical interactions.

Simulator 2840 also includes a simulator controller 2856 configured tocontrol the simulation to adapt the functionalities of anysynthetically-generated element of simulated environment 2803 todetermine cause-effect relationship, among other things. Simulator 2840includes a simulator evaluator 2858 to evaluate the performancesynthetically-generated element of simulated environment 2803. Forexample, simulator evaluator 2858 may analyze simulated vehicle commands2880 (e.g., simulated steering angles and simulated velocities) todetermine whether such commands are an appropriate response to thesimulated activities within simulated environment 2803. Further,simulator evaluator 2858 may evaluate interactions of a teleoperator2808 with the simulated autonomous vehicle 2830 via teleoperatorcomputing device 2804. Simulator evaluator 2858 may evaluate the effectsof updated reference data 2827, including updated map tiles and routedata, which may be added to guide the responses of simulated autonomousvehicle 2830. Simulator evaluator 2858 may also evaluate the responsesof simulator AV controller 2847 when policy data 2829 is updated,deleted, or added. The above-description of simulator 2840 is notintended to be limiting. As such, simulator 2840 is configured toperform a variety of different simulations of an autonomous vehiclerelative to a simulated environment, which include both static anddynamic features. For example, simulator 2840 may be used to validatechanges in software versions to ensure reliability. Simulator 2840 mayalso be used to determine vehicle dynamics properties and forcalibration purposes. Further, simulator 2840 may be used to explore thespace of applicable controls and resulting trajectories so as to effectlearning by self-simulation.

FIG. 29 is an example of a flow chart to simulate various aspects of anautonomous vehicle, according to some embodiments. Flow chart 2900begins at 2902, at which reference data including three dimensional mapdata is received into a simulator. Dynamic object data defining motionpatterns for a classified object may be retrieved at 2904. At 2906, asimulated environment is formed based on at least three dimensional(“3D”) map data and the dynamic object data. The simulated environmentmay include one or more simulated surfaces. At 2908, an autonomousvehicle is simulated that includes a simulated autonomous vehiclecontroller that forms part of a simulated environment. The autonomousvehicle controller may include a simulated perception engine and asimulated localizer configured to receive sensor data. At 2910,simulated sensor data are generated based on data for at least onesimulated sensor return, and simulated vehicle commands are generated at2912 to cause motion (e.g., vectored propulsion) by a simulatedautonomous vehicle in a synthetic environment. At 2914, simulatedvehicle commands are evaluated to determine whether the simulatedautonomous vehicle behaved consistent with expected behaviors (e.g.,consistent with a policy).

FIG. 30 is an example of a flow chart to generate map data, according tosome embodiments. Flow chart 3000 begins at 3002, at which trajectorydata is retrieved. The trajectory data may include trajectories capturedover a duration of time (e.g., as logged trajectories). At 3004, atleast localization data may be received. The localization data may becaptured over a duration of time (e.g., as logged localization data). At3006, a camera or other image sensor may be implemented to generate asubset of the localization data. As such, the retrieved localizationdata may include image data. At 3008, subsets of localization data arealigned to identifying a global position (e.g., a global pose). At 3010,three dimensional (“3D”) map data is generated based on the globalposition, and at 3012, the 3 dimensional map data is available forimplementation by, for example, a manual route data editor (e.g.,including a manual road network data editor, such as an RNDF editor), anautomated route data generator (e.g., including an automatic roadnetwork generator, including an automatic RNDF generator), a fleet ofautonomous vehicles, a simulator, a teleoperator computing device, andany other component of an autonomous vehicle service.

FIG. 31 is a diagram depicting an architecture of a mapping engine,according to some embodiments. Diagram 3100 includes a 3D mapping enginethat is configured to receive trajectory log data 3140, LIDAR log data3172, camera log data 3174, radar log data 3176, and other optionallogged sensor data (not shown). Logic 3141 includes a loop-closuredetector 3150 configured to detect whether sensor data indicates anearby point in space has been previously visited, among other things.Logic 3141 also includes a registration controller 3152 for aligning mapdata, including 3D map data in some cases, relative to one or moreregistration points. Further, logic 3141 provides data 3142 representingstates of loop closures for use by a global pose graph generator 3143,which is configured to generate pose graph data 3145. In some examples,pose graph data 3145 may also be generated based on data fromregistration refinement module 3146. Logic 3144 includes a 3D mapper3154 and a LIDAR self-calibration unit 3156. Further, logic 3144receives sensor data and pose graph data 3145 to generate 3D map data3120 (or other map data, such as 4D map data). In some examples, logic3144 may implement a truncated sign distance function (“TSDF”) to fusesensor data and/or map data to form optimal three-dimensional maps.Further, logic 3144 is configured to include texture and reflectanceproperties. 3D map data 3120 may be released for usage by a manual routedata editor 3160 (e.g., an editor to manipulate Route data or othertypes of route or reference data), an automated route data generator3162 (e.g., logic to configured to generate route data or other types ofroad network or reference data), a fleet of autonomous vehicles 3164, asimulator 3166, a teleoperator computing device 3168, and any othercomponent of an autonomous vehicle service. Mapping engine 3110 maycapture semantic information from manual annotation orautomatically-generated annotation as well as other sensors, such assonar or instrumented environment (e.g., smart stop-lights).

FIG. 32 is a diagram depicting an autonomous vehicle application,according to some examples. Diagram 3200 depicts a mobile computingdevice 3203 including an autonomous service application 3240 that isconfigured to contact an autonomous vehicle service platform 3201 toarrange transportation of user 3202 via an autonomous vehicle 3230. Asshown, autonomous service application 3240 may include a transportationcontroller 3242, which may be a software application residing on acomputing device (e.g., a mobile phone 3203, etc.). Transportationcontroller 3242 is configured to receive, schedule, select, or performoperations related to autonomous vehicles and/or autonomous vehiclefleets for which a user 3202 may arrange transportation from the user'slocation to a destination. For example, user 3202 may open up anapplication to request vehicle 3230. The application may display a mapand user 3202 may drop a pin to indicate their destination within, forexample, a geo-fenced region. Alternatively, the application may displaya list of nearby pre-specified pick-up locations, or provide the userwith a text entry field in which to type a destination either by addressor by name.

Further to the example shown, autonomous vehicle application 3240 mayalso include a user identification controller 3246 that may beconfigured to detect that user 3202 is in a geographic region, orvicinity, near autonomous vehicle 3230, as the vehicle approaches. Insome situations, user 3202 may not readily perceive or identityautonomous vehicle 3230 as it approaches for use by user 3203 (e.g., dueto various other vehicles, including trucks, cars, taxis, and otherobstructions that are typical in city environments). In one example,autonomous vehicle 3230 may establish a wireless communication link 3262(e.g., via a radio frequency (“RF”) signal, such as WiFi or Bluetooth®,including BLE, or the like) for communicating and/or determining aspatial location of user 3202 relative to autonomous vehicle 3230 (e.g.,using relative direction of RF signal and signal strength). In somecases, autonomous vehicle 3230 may detect an approximate geographiclocation of user 3202 using, for example, GPS data or the like. A GPSreceiver (not shown) of mobile computing device 3203 may be configuredto provide GPS data to autonomous vehicle service application 3240.Thus, user identification controller 3246 may provide GPS data via link3260 to autonomous vehicle service platform 3201, which, in turn, mayprovide that location to autonomous vehicle 3230 via link 3261.Subsequently, autonomous vehicle 3230 may determine a relative distanceand/or direction of user 3202 by comparing the user's GPS data to thevehicle's GPS-derived location.

Autonomous vehicle 3230 may also include additional logic to identifythe presence of user 3202, such that logic configured to perform facedetection algorithms to detect either user 3202 generally, or tospecifically identify the identity (e.g., name, phone number, etc.) ofuser 3202 based on the user's unique facial characteristics. Further,autonomous vehicle 3230 may include logic to detect codes foridentifying user 3202. Examples of such codes include specialized visualcodes, such as QR codes, color codes, etc., specialized audio codes,such as voice activated or recognized codes, etc., and the like. In somecases, a code may be an encoded security key that may be transmitteddigitally via link 3262 to autonomous vehicle 3230 to ensure secureingress and/or egress. Further, one or more of the above-identifiedtechniques for identifying user 3202 may be used as a secured means togrant ingress and egress privileges to user 3202 so as to prevent othersfrom entering autonomous vehicle 3230 (e.g., to ensure third partypersons do not enter an unoccupied autonomous vehicle prior to arrivingat user 3202). According to various examples, any other means foridentifying user 3202 and providing secured ingress and egress may alsobe implemented in one or more of autonomous vehicle service application3240, autonomous vehicle service platform 3201, and autonomous vehicle3230.

To assist user 3302 in identifying the arrival of its requestedtransportation, autonomous vehicle 3230 may be configured to notify orotherwise alert user 3202 to the presence of autonomous vehicle 3230 asit approaches user 3202. For example, autonomous vehicle 3230 mayactivate one or more light-emitting devices 3280 (e.g., LEOs) inaccordance with specific light patterns. In particular, certain lightpatterns are created so that user 3202 may readily perceive thatautonomous vehicle 3230 is reserved to service the transportation needsof user 3202. As an example, autonomous vehicle 3230 may generate lightpatterns 3290 that may be perceived by user 3202 as a “wink,” or otheranimation of its exterior and interior lights in such a visual andtemporal way. The patterns of light 3290 may be generated with orwithout patterns of sound to identify to user 3202 that this vehicle isthe one that they booked.

According to some embodiments, autonomous vehicle user controller 3244may implement a software application that is configured to controlvarious functions of an autonomous vehicle. Further, an application maybe configured to redirect or reroute the autonomous vehicle duringtransit to its initial destination. Further, autonomous vehicle usercontroller 3244 may be configured to cause on-board logic to modifyinterior lighting of autonomous vehicle 3230 to effect, for example,mood lighting. Controller 3244 may also control a source of audio (e.g.,an external source such as Spotify, or audio stored locally on themobile computing device 3203), select a type of ride (e.g., modifydesired acceleration and braking aggressiveness, modify activesuspension parameters to select a set of “road-handling” characteristicsto implement aggressive driving characteristics, including vibrations,or to select “soft-ride” qualities with vibrations dampened forcomfort), and the like. For example, mobile computing device 3203 may beconfigured to control HVAC functions as well, like ventilation andtemperature.

FIGS. 33 to 35 illustrate examples of various computing platformsconfigured to provide various functionalities to components of anautonomous vehicle service, according to various embodiments. In someexamples, computing platform 3300 may be used to implement computerprograms, applications, methods, processes, algorithms, or othersoftware to perform the above-described techniques.

Note that various structures and/or functionalities of FIG. 33 areapplicable to FIGS. 34 and 35, and, as such, some elements in thosefigures may be discussed in the context of FIG. 33.

In some cases, computing platform 3300 can be disposed in any device,such as a computing device 3390 a, which may be disposed in anautonomous vehicle 3391, and/or mobile computing device 3390 b.

Computing platform 3300 includes a bus 3302 or other communicationmechanism for communicating information, which interconnects subsystemsand devices, such as processor 3304, system memory 3306 (e.g., RAM,etc.), storage device 3308 (e.g., ROM, etc.), an in-memory cache (whichmay be implemented in RAM 3306 or other portions of computing platform3300), a communication interface 3313 (e.g., an Ethernet or wirelesscontroller, a Bluetooth controller, NFC logic, etc.) to facilitatecommunications via a port on communication link 3321 to communicate, forexample, with a computing device, including mobile computing and/orcommunication devices with processors. Processor 3304 can be implementedwith one or more graphics processing units (“GPUs”), with one or morecentral processing units (“CPUs”), such as those manufactured by Intel®Corporation, or one or more virtual processors, as well as anycombination of CPUs and virtual processors. Computing platform 3300exchanges data representing inputs and outputs via input-and-outputdevices 3301, including, but not limited to, keyboards, mice, audioinputs (e.g., speech-to-text devices), user interfaces, displays,monitors, cursors, touch-sensitive displays, LCD or LED displays, andother I/O-related devices.

According to some examples, computing platform 3300 performs specificoperations by processor 3304 executing one or more sequences of one ormore instructions stored in system memory 3306, and computing platform3300 can be implemented in a client-server arrangement, peer-to-peerarrangement, or as any mobile computing device, including smart phonesand the like. Such instructions or data may be read into system memory3306 from another computer readable medium, such as storage device 3308.In some examples, hard-wired circuitry may be used in place of or incombination with software instructions for implementation. Instructionsmay be embedded in software or firmware. The term “computer readablemedium” refers to any tangible medium that participates in providinginstructions to processor 3304 for execution. Such a medium may takemany forms, including but not limited to, nonvolatile media and volatilemedia. Non-volatile media includes, for example, optical or magneticdisks and the like. Volatile media includes dynamic memory, such assystem memory 3306.

Common forms of computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can read. Instructions may further be transmittedor received using a transmission medium. The term “transmission medium”may include any tangible or intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible medium to facilitate communication of such instructions.Transmission media includes coaxial cables, copper wire, and fiberoptics, including wires that comprise bus 3302 for transmitting acomputer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 3300. According to some examples,computing platform 3300 can be coupled by communication link 3321 (e.g.,a wired network, such as LAN, PSTN, or any wireless network, includingWiFi of various standards and protocols, Bluetooth®, NFC, Zig-Bee, etc.)to any other processor to perform the sequence of instructions incoordination with (or asynchronous to) one another. Computing platform3300 may transmit and receive messages, data, and instructions,including program code (e.g., application code) through communicationlink 3321 and communication interface 3313. Received program code may beexecuted by processor 3304 as it is received, and/or stored in memory3306 or other non-volatile storage for later execution.

In the example shown, system memory 3306 can include various modulesthat include executable instructions to implement functionalitiesdescribed herein. System memory 3306 may include an operating system(“O/S”) 3332, as well as an application 3336 and/or logic module(s)3359. In the example shown in FIG. 33, system memory 3306 includes anautonomous vehicle (“AV”) controller module 3350 and/or its components(e.g., a perception engine module, a localization module, a plannermodule, and/or a motion controller module), any of which, or one or moreportions of which, can be configured to facilitate an autonomous vehicleservice by implementing one or more functions described herein.

Referring to the example shown in FIG. 34, system memory 3306 includesan autonomous vehicle service platform module 3450 and/or its components(e.g., a teleoperator manager, a simulator, etc.), any of which, or oneor more portions of which, can be configured to facilitate managing anautonomous vehicle service by implementing one or more functionsdescribed herein.

Referring to the example shown in FIG. 35, system memory 3306 includesan autonomous vehicle (“AV”) module and/or its components for use, forexample, in a mobile computing device. One or more portions of module3550 can be configured to facilitate delivery of an autonomous vehicleservice by implementing one or more functions described herein.

Referring back to FIG. 33, the structures and/or functions of any of theabove-described features can be implemented in software, hardware,firmware, circuitry, or a combination thereof. Note that the structuresand constituent elements above, as well as their functionality, may beaggregated with one or more other structures or elements. Alternatively,the elements and their functionality may be subdivided into constituentsub-elements, if any. As software, the above-described techniques may beimplemented using various types of programming or formatting languages,frameworks, syntax, applications, protocols, objects, or techniques. Ashardware and/or firmware, the above-described techniques may beimplemented using various types of programming or integrated circuitdesign languages, including hardware description languages, such as anyregister transfer language (“RTL”) configured to designfield-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs’), or any other type of integrated circuit.According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof. These can bevaried and are not limited to the examples or descriptions provided.

In some embodiments, module 3350 of FIG. 33, module 3450 of FIG. 34, andmodule 3550 of FIG. 35, or one or more of their components, or anyprocess or device described herein, can be in communication (e.g., wiredor wirelessly) with a mobile device, such as a mobile phone or computingdevice, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with one or more modules 3359 (module 3350 ofFIG. 33, module 3450 of FIG. 34, and module 3550 of FIG. 35) or one ormore of its components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figures can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, module 3350 of FIG. 33, module 3450 of FIG. 34, and module3550 of FIG. 35, or one or more of its components, or any process ordevice described herein, can be implemented in one or more computingdevices (i.e., any mobile computing device, such as a wearable device,an audio device (such as headphones or a headset) or mobile phone,whether worn or carried) that include one or more processors configuredto execute one or more algorithms in memory. Thus, at least some of theelements in the above-described figures can represent one or morealgorithms. Or, at least one of the elements can represent a portion oflogic including a portion of hardware configured to provide constituentstructures and/or functionalities. These can be varied and are notlimited to the examples or descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit.

For example, module 3350 of FIG. 33, module 3450 of FIG. 34, and module3550 of FIG. 35, or one or more of its components, or any process ordevice described herein, can be implemented in one or more computingdevices that include one or more circuits. Thus, at least one of theelements in the above-described figures can represent one or morecomponents of hardware. Or, at least one of the elements can represent aportion of logic including a portion of a circuit configured to provideconstituent structures and/or functionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (Le., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

FIGS. 36A to 36B illustrate a high-level block diagram depicting anautonomous vehicle system experiencing a sensor-based anomaly while inoperation, according to various embodiments. An autonomous vehiclesystem 3602 may be surrounded by many moving vehicles 3600 (e.g., movingvehicles 3600 a, 3600 b, and 3600 c) in a typical driving scenario. Asmentioned above, with respect to FIG. 3A, the autonomous vehicle system3602 may include many types of sensors or any quantity of sensors tofacilitate perception, including image capture sensors, audio capturesensors, LIDAR, RADAR, GPS, and IMU. As illustrated in FIG. 36A, LIDARsensors 3604 (e.g., LIDAR sensors 3604A, 36048, 3604C, and 36040) maycapture distance, image intensity, and 3D point cloud data based onlaser returns 3608.

An example LIDAR sensor 3604 is a VELODYNE VLP-16 Real-Time 3D LIDARSensor, manufactured by Velodyne Acoustics, Inc. in Morgan Hill, Calif.A LIDAR Sensor 3604 creates 360 degree 3D images by using 16laser/detector pairs mounted in a compact housing. The housing rapidlyspins to scan the surrounding environment. The lasers fire thousands oftimes per second, providing a rich 3D point cloud in real time. A laserreturn 3608 provides data about the reflectivity of an object with256-bit resolution independent of laser power and distance over a rangefrom 1 meter to 100 meters. For example, laser returns 3608 from LIDARsensors 3604A and 3604B may be used to identify labeled points 3606 band 3606 c on moving vehicles 3600 b and 3600 c, respectively. Datarepresenting laser returns 3608 may be stored and processed in theautonomous vehicle system 3602, as described above. A LIDAR sensor 3604may be synchronized with GPS data based on GPS-supplied time pulses,enabling the ability to determine the exact GPS location at the time ofeach firing time of each laser. This enables data representing laserreturns 3608 to be geo-referenced in real-time. In another embodiment, aLIDAR sensor may be a single-beam LIDAR sensor.

As described above, an autonomous vehicle system 3602 uses varioussensors, including LIDAR sensors 3604, in a perception system to aid inlocalizing the autonomous vehicle system 3602. For safety reasons andoperational efficiency, the autonomous vehicle system 3602 determinesits location and the surrounding environment, such as static objectslike lane markings 3610 and curbs 3612 as well as dynamic objects likemoving vehicles 3600 in real-time and continuously. The autonomousvehicle system 3602 relies on sensor data to update map tileinformation, such as new lane markings 3610, as well as currentlocalized information, such as three moving vehicles 3600 also travelingin the same direction of travel 3614 as the autonomous vehicle 3602.

Illustrated in FIG. 36A, there are two LIDAR sensors 3604 operating, inredundancy, at the front end of the autonomous vehicle system 3602, inrelation to the direction of travel 3614. In other embodiments,additional or fewer LIDAR sensors 3604 may be used, and in various otherarrangements, such as a curved array of LIDAR sensors 3604, and/or twoLIDAR sensors 3604 at each corner of the autonomous vehicle 3602.However, as illustrated in FIG. 36A, a LIDAR sensor 3604A and a LIDARsensor 36048 are each mounted at the corners of the autonomous vehiclesystem 3602. The LIDAR sensors 3604 have been calibrated such that thelaser returns 3608 identify labeled points 3606 accurately andindependently, in real-time with synchronized GPS data.

FIG. 36B illustrates a scenario in which one of the LIDAR sensors 3604experiences an anomaly or other malfunction. A sensor anomaly may bedetected when sensor data may be still gathered from the sensor, but thedata may be outside of normal operating parameters. Here, failed sensor3604 b has reported an indication that the data from the sensor cannotbe trusted for various reasons, such as the data being reported isseverely miscalibrated, the data being reported does not match expecteddata (such as a lane marking 3610 that is not in the expected field ofperception), as well as complete sensor failure that reports no data ora sensor malfunction. As a result of failed sensor 3604 b, LIDAR sensor3604A is relied upon by the autonomous vehicle system 3602 forgenerating a field of perception until the failed sensor 3604 b becomesoperational again. In one embodiment, the LIDAR sensor 3604A generateslaser returns 3608 that form the field of perception for the autonomousvehicle system 3602 after the autonomous vehicle system 3602 receives anindication of the failed sensor 3604 b.

In one embodiment, the autonomous vehicle system 3602 (“AV system”) mayinclude a sensor failure strategies store 3718, as illustrated in FIG.37. FIG. 37 illustrates a high-level block diagram of a sensor-basedobject detection optimization for autonomous vehicles, according tovarious embodiments. Upon receiving an indication of an anomaly and/orfailure of a sensor 3714, which may include LIDAR sensors 3604, RADARsensors 3708, 1M Us 3712, and cameras 3710, a perception system 3702 mayutilize a sensor failure module 3704 to process the sensor anomalyand/or failure. A localizer 3716 may include a sensor compensationmodule 3706, in one embodiment, to process the incoming sensor datareceived from sensors 3714 and to identify how the field of perceptionfor the AV system 3602 is affected by the failed sensor. The AV system3602 may rely on the sensors 3714 and “fuse” the data generated by theheterogeneous types of sensors, such as data from LIDAR sensors 3604 andmotion data from IMUs 3712, in one embodiment. The localizer 3716 maygenerate a probabilistic map of the current environment, assigningprobability scores to labeled objects in the field of perception. Theseprobability scores may be reduced as a result of the failed sensor. Asensor compensation module 3706 may be able to rely on other sensor datato compensate for the lost data stream of the failed sensor. Forexample, IMU motion data may indicate that the AV system 3602 istraveling in a straight line at a certain velocity such that theexpected field of perception may be calculated by the sensorcompensation module 3706 using the remaining operational LIDAR sensor3604.

An AV system 3602 may also include a planner 3722 that includes atrajectory selection module 3724. The planner 3722 may retrieve one ormore strategies from the sensor failure strategies store 3718 todetermine one or more trajectories for the AV system 3602. For example,if the AV system 3602 is currently executing a ride request from apassenger user, the planner 3722 may determine whether the perceptionsystem 3702 is operating within safe parameters such that the ride maybe completed. In one embodiment, the planner 3722 may decide to executea maneuver to change the directionality of the AV system 3602 such thatthe failed sensor is located at the dorsal end, or the rear, of the AVsystem 3602 in relation to the direction of travel. An example of such amaneuver is described above in relation to FIG. 3E, where the failedsensor 309 is initially located at the front of the AV system, but thenis located at the rear after performing the maneuver.

As described above with respect to FIG. 4 and now with respect to FIG.37, a controller 3726 may be used by an AV system 3602 to control themotion of the vehicle. A log file store 3720 may be used to store sensordata generated by the sensors 3714 as well as trajectories executed bythe planner 3722 based on the received sensor data. In one embodiment,the planner 3722 may communicate with a teleoperator system 3750 to askfor additional guidance based on the indication of the failed sensor.One or more trajectories may have been preselected and presented to theteleoperator system 3750 based on a sensor failure strategy retrievedfrom the sensor failure strategies store 3718. In another embodiment,the planner 3722 may rely on log file data retrieved from the log filestore 3720 to determine and/or select a trajectory for the AV system3602 to travel. These determinations and calculations may be performedonline and in real-time as the vehicle is moving and in operation, inone embodiment. In another embodiment, a sensor failure strategy mayinclude an instruction to arrive at a safe stop based on a failedsensor. After the safe stop, the AV system 3602 may execute one or morecourses of action based on the sensor failure strategy retrieved fromthe store 3718.

In a further embodiment, the AV system 3602 may use one or more sensorfailure strategies retrieved from the sensor failure strategies store3718 and have the planner 3722 decide a course of action from a selectedstrategy based on log file data retrieved from the log file store 3720and/or sensor data being generated from sensors 3714. The sensor failuremodule 3704 may report data generated from the sensors 3714, includingthe failed sensor data and/or anomalous data to the localizer 3716. Thelocalizer 3716 may assign a probability score of the likelihood of anobject being correctly labeled within the field of perception, such aslabeled point 3606 b being correctly correlated with moving vehicle 3600b. Probability scores such as this may be generated from a sensorcompensation module 3706 based on past sensor failures, in oneembodiment. In another embodiment, a localizer 3716, as described abovewith respect to FIG. 4, may generate probabilistic map tiles that assignprobabilities to each map tile, which may be 16 cm×16 cm, in oneembodiment. As a result, the laser returns 3608 of LIDAR sensor 3604A,as illustrated in FIG. 368, may be solely relied upon for the localizer3716 generating a map tile associated with the labeled points 3606, forexample. In another embodiment, IMU data indicating the velocity of theAV system 3602 along with GPS data and log file data may be also be usedto generate the probability score for the labeled points 3606. The AVsystem 3602 is able to compute these probabilities in real-time, as thevehicle is in operation, because of processing power of on-boardcomputers, super-fast processors, and low-latency log file retrieval andstorage.

Referring to FIGS. 3B to 3D, each sensor 310 may generate a sensor field301. Sensor fields 301 may overlap, such that combined sensor fields 302and 303, as illustrated in FIG. 3C, may two or more sensors 310contributing to the object classification and/or object detectionalgorithms. However, these sensor fields may change based on a sensorfailure or other sensor anomaly, as illustrated in FIG. 3D where sensor309 has failed. As shown, a blind spot 304 in the field of perceptionmay occur based on a failed sensor 309. The field of perception, asillustrated in FIG. 3D, includes sensor fields 301, 302, 303, 305, and306. Other types of sensors may be used to compensate for a blind spot304 in the field of perception while the AV system is in operation.

In an embodiment, a sensor anomaly may be detected upon the AV systemdetermining that sensor data gathered from the sensor includes a rangeof laser return intensities that are a result of the reflectivityproperties various phenomena, such as too much sunlight, directly orindirectly bouncing off glass, water, or other shiny surfaces, and otherlights, such as headlights or external lights in the infrared rangeinterfering with the laser returns. Various conditions, such as weathercondition, refraction due to differences in air density due to heatwaves off a hot road surface, and glass surfaces with water (e.g.,windshields, foggy and/or wet windshields) may cause interferences withLIDAR sensors, reducing the range at which light may be received. Otherconditions, such as bright direct sunlight and/or high intensityheadlights, may impede laser returns because the sensor's receiverrejects light outside the laser's operating range.

Returning to FIG. 37, a sensor recovery strategies store 3718 may bestored locally on the A V system 3602 and updated with new sensorrecovery strategies as they become available. Such sensor recoverystrategies may be employed based on real-life scenarios, in oneembodiment. For example, log files included in the log file store 3720may be analyzed to produce one or more sensor recovery strategies to bestored in the sensor recovery strategies store 3718. Other sensorrecovery strategies may be simulated offline using a simulator thatreplicates real driving conditions in a simulated world.

Additional sensor recovery strategies may include accessing a mapdatabase to determine an alternative trajectory, route, and/or path thatavoids one or more conditions that may be causing the anomalous sensormeasurements. For example, where the sensor measurements of laserreturns may be attributed to too much direct sunlight, alternative pathsand/or trajectories may be determined as a sensor recovery strategyincluded in the store 3718. The planner 3722 may then select a newtrajectory through the trajectory selection module 3724 that includesless direct sunlight based on building blocking the direct sunlight. Asa result, the detection of the cause of the sensor anomaly may influencethe selection of a sensor recovery strategy, thus modifying theoperation of the AV system responsive to the sensor anomaly.

In one embodiment, the localizer 3716 may determine a quantifiablemeasure of how the perception system 3702 is affected by the sensoranomaly and/or failure. A course of action in a sensor recovery strategystore 3718 may include operating the AV system 3602 in a sub-optimalmode of operation that remains within safe levels of operation. In thissub-optimal mode of operation, other sensors may be used and relied uponto ensure a safe level of operation, such as using camera data inconjunction with IMU motion data to compensate for blind spots caused bya failed sensor.

The sensor compensation module 3706 may include various methods andtechniques that may be used to compensate for different types of sensoranomalies and/or failures. Thus, the type of sensor anomaly or type ofsensor failure, as determined by the sensor failure module 3704, maydirectly affect how the localizer 3716 uses other sensor data tocompensate for the loss and/or degradation in the field of perception.For example, processing camera data may be prioritized based on a failedLIDAR sensor at the front of the AV system 3602. Similarly, generatingprobabilistic map tiles based on inferred labeled data points using IMUmotion data with a remaining operational LIDAR sensor in conjunctionwith GPS data may be highly prioritized by a processor or set ofprocessors in the AV system 3602, as another example. The sensorcompensation module 3706 may identify a degradation of a sensor based onany number of conditions, such as bad weather, low battery, low memoryutilization, and low storage capacity, and may provide coverage for thelower functioning sensor based on other sensors.

FIG. 38 is a network diagram of a system for sensor-based objectdetection optimization for autonomous vehicles, showing a block diagramof an autonomous vehicle management system, according to an embodiment.The system environment includes one or more AV systems 3602,teleoperator systems 3750, user devices 3806, an autonomous vehicle(“AV”) management system 3800, and a network 3804. In alternativeconfigurations, different and/or additional modules can be included inthe system.

The user devices 3806 may include one or more computing devices that canreceive user input and can transmit and receive data via the network3804. In one embodiment, the user device 3806 is a conventional computersystem executing, for example, a Microsoft Windows-compatible operatingsystem (OS), Apple OS X, and/or a Linux distribution. In anotherembodiment, the user device 3806 can be a device having computerfunctionality, such as a personal digital assistant (PDA), mobiletelephone, smart-phone, wearable device, etc. The user device 3806 isconfigured to communicate via network 3804. The user device 3806 canexecute an application, for example, a browser application that allows auser of the user device 3806 to interact with the AV management system3800. In another embodiment, the user device 3806 interacts with the AVmanagement system 3800 through an application programming interface(API) that runs on the native operating system of the user device 3806,such as iOS and ANDROID.

In one embodiment, the network 3804 uses standard communicationstechnologies and/or protocols. Thus, the network 3804 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (Wi MAX), 3G, 4G, CDMA, digital subscriber line(DSL), etc. Similarly, the networking protocols used on the network 3804can include multiprotocol label switching (MPLS), the transmissioncontrol protocol/Internet protocol (TCP/IP), the User Datagram Protocol(UDP), the hypertext transport protocol (HTTP), the simple mail transferprotocol (SMTP), and the file transfer protocol (FTP). The dataexchanged over the network 204 can be represented using technologiesand/or formats including the hypertext markup language (HTML) and theextensible markup language (XML). (in addition, all or some of links canbe encrypted using conventional encryption technologies such as securesockets layer (SSL), transport layer security (TLS), and InternetProtocol security (IPsec).

FIG. 38 contains a block diagram of the AV management system 3800. TheAV management system 3800 includes a sensor recovery scenario store3802, a web server 3810, an API management module 3808, a sensorrecovery module 3812, and a teleoperator interface module 3814. In otherembodiments, the AV management system 3800 may include additional,fewer, or different modules for various applications. Conventionalcomponents such as network interfaces, security functions, loadbalancers, failover servers, management and network operations consoles,and the like are not shown so as to not obscure the details of thesystem.

The web server 3810 links the AV management system 3800 via the network3804 to one or more user devices 3806; the web server 3810 serves webpages, as well as other web-related content, such as Java, Flash, XML,and so forth. The web server 3810 may provide the functionality ofreceiving and routing messages between the AV management system 3800 andthe user devices 3806, for example, instant messages, queued messages(e.g., email), text and SMS (short message service) messages, ormessages sent using any other suitable messaging technique. The user cansend a request to the web server 3810 to provide information, forexample, images or videos that are stored in the AV management system3800 for viewing by the user device(s) 3806. Additionally, the webserver 3810 may provide API functionality to send data directly tonative user device operating systems, such as iOS, ANDROID, webOS, andRIM.

A sensor recovery scenario store 3802 may store sensor recoveryscenarios uploaded by AV systems 3602 connected to the AV managementsystem 3800. Sensor recovery scenarios may include sensor data capturedduring a sensor failure or abnormal sensor operation, as well as coursesof action taken by the AV systems 3602 in response to the sensorfailures. Additionally, sensor recovery scenarios may be generated andstored by other systems connected to or part of the AV management system3800, such as simulated scenarios in which suggested strategies havebeen executed, in a simulated environment, by various simulated AVsystems (not pictured). Based on these scenarios, a sensor recoverymodule 3812 may be configured to provide recommended courses of actionand/or strategies to respond to various types of sensor failures. Theserecommendations may be provided to a teleoperator system 3750 requestinghistorical data and/or past strategies for handling sensor failures, forexample. In another embodiment, such recommendations may be provided toA V systems 3602 based on an indication of a sensor failure.

An API management module 3808 may manage one or more adapters needed forthe AV management system 3800 to communicate with various systems, suchas teleoperator systems 3750 and user devices 3806. Applicationprogramming interfaces (APIs), or adapters, may be used to push data toexternal tools, websites, and user devices 3806. Adapters may also beused to receive data from the external systems. In one embodiment, theAPI management module 3808 manages the amount of connections to theexternal systems needed to operate efficiently.

A sensor recovery module 3812 may analyze and provide information to AVsystems 3602 based on received log data and/or indications from the AVsystems 3602 that one or more sensors have malfunctioned, in oneembodiment. The sensor recovery module 3812 may process the datagathered from multiple AV systems 3602 offline and may rely on variousprobabilistic techniques, Bayesian inference, and machine learningalgorithms to identify optimal strategies in responding to sensorfailures over time.

A teleoperator interface module 3814 may provide an interface forteleoperator systems 3750 to interact with and provide guidance to AVsystems 3602. In conjunction with the sensor recovery module 3812, theteleoperator interface module 3814 may provide one or more selectedstrategies to a teleoperator system 3750 that has been requested toprovide assistance to an AV system 3602 experiencing a sensor anomalyand/or failure. For example, an AV system 3602 may detect that a sensoris not operating within normal parameters based on data generated fromthe sensor not corroborating with other sensor data, such as a lanemarking appearing in an unexpected location based on map tileinformation. In presenting the sensor information from the AV system3602 to the teleoperator system 3750, the teleoperator interface module3814 may retrieve log data and/or other information related to similarsensor anomalies from the sensor failure scenario store 3802. As aresult, the teleoperator system 3750 may confirm, through theteleoperator interface provided by the teleoperator interface module3814, that the sensor has failed. A suggested course of action may beselected through the teleoperator interface, in one embodiment. Inanother embodiment, the AV system 3602 may automatically identify acourse of action based on the confirmed sensor failure.

FIG. 39 is a high-level flow diagram illustrating a process forsensor-based object detection optimization for autonomous vehicles,according to some examples. An indication of a sensor anomaly may bereceived 3900 at an autonomous vehicle system. The indication of asensor anomaly may be received 3900 based on a determination that sensordata measurements gathered from a particular sensor may not be correctbased on expected measurements. In another embodiment, sensor data for aparticular sensor may not be received, indicating that the sensor hasfailed or is otherwise malfunctioning. At least one sensor recoverystrategy may be determined 3902 based on the sensor anomaly. Sensorrecovery strategies may be pre-generated based on the arrangement ofsensors on the autonomous vehicle system and stored locally on theautonomous vehicle system. In one embodiment, a sensor recovery strategymay be determined 3902 by retrieving and/or receiving strategies fromanother autonomous vehicle system and/or an autonomous vehiclemanagement system 3800.

Optionally, a guidance request may be sent 3904 to a teleoperator systemand/or an AV management system based on the at least one sensor recoverystrategy and the sensor anomaly. This guidance request may be a requestfor more information, such as historical log data of similar sensoranomalies and/or failures, in one embodiment. In another embodiment, theguidance request may include a request for a suggestion on a course ofaction. The suggestion may be received 3906, optionally, based on theguidance request in association with the sensor anomaly. The suggestionmay be made through a selection on teleoperator interface, for example,provided on a teleoperator system. In one embodiment, a suggestion maybe made based on offline analysis by an AV management system of pastsensor anomalies and/or failures. A course of action included in the atleast one sensor recovery strategy may be executed 3908. Optionally, thecourse of action may be executed 3908 in accordance with the receivedsuggestion.

FIGS. 40 and 41 illustrate exemplary computing platforms disposed indevices configured to optimize sensor-based object detection inaccordance with various embodiments. In some examples, computingplatforms 4000 and 4100 may be used to implement computer programs,applications, methods, processes, algorithms, or other software toperform the above-described techniques.

In some cases, computing platform can be disposed in wearable device 10or implement, a mobile computing device 4090 b or 4190 b, or any otherdevice, such as a computing device 4090 a or 4190 a.

Computing platform 4000 or 4100 includes a bus 4004 or 4104 or othercommunication mechanism for communicating information, whichinterconnects subsystems and devices, such as processor 4006 or 4106,system memory 4010 or 4110 (e.g., RAM, etc.), storage device 4008 or4108 (e.g., ROM, etc.), a communication interface 4012 or 4112 (e.g., anEthernet or wireless controller, a Bluetooth controller, etc.) tofacilitate communications via a port on communication link 4014 or 4114to communicate, for example, with a computing device, including mobilecomputing and/or communication devices with processors. Processor 4006or 4106 can be implemented with one or more central processing units(“CPUs”), such as those manufactured by Intel® Corporation, or one ormore virtual processors, as well as any combination of CPUs and virtualprocessors. Computing platform 4000 or 4100 exchanges data representinginputs and outputs via input-and-output devices 4002 or 4102, including,but not limited to, keyboards, mice, audio inputs (e.g., speech-to-textdevices), user interfaces, displays, monitors, cursors, touch-sensitivedisplays, LCD or LED displays, and other I/O-related devices.

According to some examples, computing platform 4000 or 4100 performsspecific operations by processor 4006 or 4106 executing one or moresequences of one or more instructions stored in system memory 4010 or4110, and computing platform 4000 or 4100 can be implemented in aclient-server arrangement, peer-to-peer arrangement, or as any mobilecomputing device, including smart phones and the like. Such instructionsor data may be read into system memory 4010 or 4110 from anothercomputer readable medium, such as storage device 4008 or 4108. In someexamples, hard-wired circuitry may be used in place of or in combinationwith software instructions for implementation. Instructions may beembedded in software or firmware. The term “computer readable medium”refers to any tangible medium that participates in providinginstructions to processor 4006 or 4106 for execution. Such a medium maytake many forms, including but not limited to, non-volatile media andvolatile media. Non-volatile media includes, for example, optical ormagnetic disks and the like. Volatile media includes dynamic memory,such as system memory 4010 or 4110.

Common forms of ‘computer readable media includes, for example, floppydisk, flexible disk, hard disk, magnetic tape, any other magneticmedium, CD-ROM, any other optical medium, punch cards, paper tape, anyother physical medium with patterns of holes, RAM, PROM, EPROM,FLASH-EPROM, any other memory chip or cartridge, or any other mediumfrom which a computer can read. Instructions may further be transmittedor received using a transmission medium. The term “transmission medium”may include any tangible or intangible medium that is capable ofstoring, encoding or carrying instructions for execution by the machine,and includes digital or analog communications signals or otherintangible medium to facilitate communication of such instructions.Transmission media includes coaxial cables, copper wire, and fiberoptics, including wires that comprise bus 4004 or 4104 for transmittinga computer data signal.

In some examples, execution of the sequences of instructions may beperformed by computing platform 4000 or 4100. According to someexamples, computing platform 4000 or 4100 can be coupled bycommunication link 4014 or 4114 (e.g., a wired network, such as LAN,PSTN, or any wireless network, including WiFi of various standards andprotocols, Blue Tooth®, Zig-Bee, etc.) to any other processor to performthe sequence of instructions in coordination with (or asynchronous to)one another. Computing platform 4000 or 4100 may transmit and receivemessages, data, and instructions, including program code (e.g.,application code) through communication link 4014 or 4114 andcommunication interface 4012 or 4112. Received program code may beexecuted by processor 4006 or 4106 as it is received, and/or stored inmemory 4010 or 4110 or other non-volatile storage for later execution.

In the example shown, system memory 4010 or 4110 can include variousmodules that include executable instructions to implementfunctionalities described herein. System memory 4010 or 4110 may includean operating system (“O/S”) 4030 or 4130, as well as an application 4032or 4132 and/or logic module 4050 or 4150. In the example shown in FIG.40, system memory 4010 includes a sensor recovery module 3812, an APImanagement module 3808, and a teleoperator interface module 3814. Thesystem memory 4150 shown in FIG. 41 includes a perception system 3702that includes a sensor failure module 3704, a localizer module 4134 thatincludes a sensor compensation module 3706, and a planner module 4136that includes a trajectory selection module 3724. One or more of themodules included in memory 4010 or 4110 can be configured to provide orconsume outputs to implement one or more functions described herein.

In at least some examples, the structures and/or functions of any of theabove-described features can be implemented in software, hardware,firmware, circuitry, or a combination thereof. Note that the structuresand constituent elements above, as well as their functionality, may beaggregated with one or more other structures or elements. Alternatively,the elements and their functionality may be subdivided into constituentsub-elements, if any. As software, the above-described techniques may beimplemented using various types of programming or formatting languages,frameworks, syntax, applications, protocols, objects, or techniques. Ashardware and/or firmware, the above-described techniques may beimplemented using various types of programming or integrated circuitdesign languages, including hardware description languages, such as anyregister transfer language (“RTL”) configured to designfield-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), or any other type of integrated circuit.According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof. These can bevaried and are not limited to the examples or descriptions provided.

In some embodiments, an AV management system or one or more of itscomponents, or any process or device described herein, can be incommunication (e.g., wired or wirelessly) with a mobile device, such asa mobile phone or computing device, or can be disposed therein.

In some cases, a mobile device, or any networked computing device (notshown) in communication with an action alert controller or one or moreof its components (or any process or device described herein), canprovide at least some of the structures and/or functions of any of thefeatures described herein. As depicted in the above-described figures,the structures and/or functions of any of the above-described featurescan be implemented in software, hardware, firmware, circuitry, or anycombination thereof. Note that the structures and constituent elementsabove, as well as their functionality, may be aggregated or combinedwith one or more other structures or elements. Alternatively, theelements and their functionality may be subdivided into constituentsub-elements, if any. As software, at least some of the above-describedtechniques may be implemented using various types of programming orformatting languages, frameworks, syntax, applications, protocols,objects, or techniques. For example, at least one of the elementsdepicted in any of the figure can represent one or more algorithms. Or,at least one of the elements can represent a portion of logic includinga portion of hardware configured to provide constituent structuresand/or functionalities.

For example, a perception system 3702 or any of its one or morecomponents, or any process or device described herein, can beimplemented in one or more computing devices (i.e., any mobile computingdevice, such as a wearable device, an audio device (such as headphonesor a headset) or mobile phone, whether worn or carried) that include oneor more processors configured to execute one or more algorithms inmemory. Thus, at least some of the elements in the above-describedfigures can represent one or more algorithms. Or, at least one of theelements can represent a portion of logic including a portion ofhardware configured to provide constituent structures and/orfunctionalities. These can be varied and are not limited to the examplesor descriptions provided.

As hardware and/or firmware, the above-described structures andtechniques can be implemented using various types of programming orintegrated circuit design languages, including hardware descriptionlanguages, such as any register transfer language (“RTL”) configured todesign field-programmable gate arrays (“FPGAs”), application-specificintegrated circuits (“ASICs”), multi-chip modules, or any other type ofintegrated circuit.

For example, an autonomous vehicle management system, including one ormore components, or any process or device described herein, can beimplemented in one or more computing devices that include one or morecircuits. Thus, at least one of the elements in the above-describedfigures can represent one or more components of hardware. Or, at leastone of the elements can represent a portion of logic including a portionof circuit configured to provide constituent structures and/orfunctionalities.

According to some embodiments, the term “circuit” can refer, forexample, to any system including a number of components through whichcurrent flows to perform one or more functions, the components includingdiscrete and complex components. Examples of discrete components includetransistors, resistors, capacitors, inductors, diodes, and the like, andexamples of complex components include memory, processors, analogcircuits, digital circuits, and the like, including field-programmablegate arrays (“FPGAs”), application-specific integrated circuits(“ASICs”). Therefore, a circuit can include a system of electroniccomponents and logic components (e.g., logic configured to executeinstructions, such that a group of executable instructions of analgorithm, for example, and, thus, is a component of a circuit).According to some embodiments, the term “module” can refer, for example,to an algorithm or a portion thereof, and/or logic implemented in eitherhardware circuitry or software, or a combination thereof (i.e., a modulecan be implemented as a circuit). In some embodiments, algorithms and/orthe memory in which the algorithms are stored are “components” of acircuit. Thus, the term “circuit” can also refer, for example, to asystem of components, including algorithms. These can be varied and arenot limited to the examples or descriptions provided.

Although the foregoing examples have been described in some detail forpurposes of clarity of understanding, the above-described inventivetechniques are not limited to the details provided. There are manyalternative ways of implementing the above-described inventiontechniques. The disclosed examples are illustrative and not restrictive.

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method comprising: receiving, at an autonomousvehicle system comprising a plurality of sensors, an indication of asensor anomaly or a sensor failure associated with a first sensor of theplurality of sensors; receiving, at the autonomous vehicle from a subsetof sensors, data representing information about an environment of theautonomous vehicle, the subset of sensors comprising one or more workingand non-anomalous sensors of the plurality of sensors exclusive of thefirst sensor; determining, based at least in part on the indication andthe data representing information about an environment of the autonomousvehicle received from the subset of sensors comprising the one or moreworking and non-anomalous sensors of the plurality of sensors exclusiveof the first sensor, an impact on a perception of the autonomous vehiclecaused by the sensor anomaly or the sensor failure associated with thefirst sensor; determining, based at least in part on the impact on theperception of the autonomous vehicle, a sensor recovery strategy; andcontrolling the autonomous vehicle based at least in part on the sensorrecovery strategy.
 2. The method of claim 1, wherein the anomalycomprises one or more of a sensor malfunction, receiving no data,receiving data outside of normal operating parameters, a sensormiscalibration, or a discrepancy from expected sensor data.
 3. Themethod of claim 1, wherein the sensor recovery strategy comprises one ofa plurality of sensor recover strategies, the plurality of sensorrecover strategies comprising at least one of: a first sensor recoverystrategy computed from one or more log files, the log files comprisingdata obtained by the plurality of sensors about a second environmentsubstantially similar to the environment; or a second sensor recoverystrategy computed from one or more simulations, the simulationscomprising simulated data about a simulated environment substantiallysimilar to the environment.
 4. The method of claim 1, wherein theplurality of sensors comprises a plurality of first sensors of a firsttype and one or more second sensors of a second type, the plurality offirst sensors comprising the sensor and the subset of sensors andwherein the data comprises first data, the method further comprising:receiving, at the autonomous vehicle from one or more second sensors,second data representing information about the environment, the sensorrecovery strategy comprising, at least in part, using the second data tocompensate for lost data received from the first sensor in the absenceof the anomaly or the failure.
 5. The method of claim 4, wherein thefirst type comprises one of a lidar, a RADAR sensor, a camera, or aninertial measurement unit and the second type comprises another of thelidar, the RADAR sensor, the camera, or the initial measurement unit. 6.The method of claim 4, further comprising determining the impact on theperception of the autonomous vehicle at least in part by: generatingprobabilistic map data using the first data and the second data.
 7. Themethod of claim 1, further comprising determining the sensor anomalybased at least in part on a comparison of data from the first sensorwith expected data.
 8. The method of claim 1, wherein the sensorrecovery strategy comprises requesting assistance from a teleoperatorsystem, and the method further comprises: receiving a trajectory for theautonomous vehicle, the trajectory being selected, at least in part, onthe sensor anomaly.
 9. The method of claim 1, wherein the sensorrecovery strategy comprises navigating a trajectory that optimizescollection of the information about the environment at the subset of thesensors.
 10. A system comprising: an autonomous vehicle; a plurality ofsensors disposed on the autonomous vehicle, the plurality of sensorsbeing configured to sense objects in an environment of the autonomousvehicle, the plurality of sensors comprising a plurality of firstsensors of a first sensor type and one or more sensors of a secondsensor type; a computing system communicatively coupled to theautonomous vehicle, the computing system being configured to: receive anindication of a sensor anomaly or a sensor failure associated with afirst sensor of the plurality of first sensors; receive first sensorinformation from the one or more working and non-anomalous sensors ofthe plurality of first sensors of the first sensor type excluding thefirst sensor; receive second sensor information from the one or moresensors of the second sensor type; determine, based at least in part onthe indication, the first sensor information received from the one ormore working and non-anomalous sensors of the plurality of first sensorsof the first sensor type, and the second sensor information, an impacton a perception of the autonomous vehicle caused by the sensor anomalyor the sensor failure associated with the first sensor; determine, basedat least in part on the impact on the perception of the autonomousvehicle, a sensor recovery strategy to compensate for the sensor anomalyor the sensor failure; and implement the sensor recovery strategy tocompensate for the sensor anomaly or the sensor failure.
 11. The systemof claim 10, the computing system being further configured to determinethe impact on the perception of the autonomous vehicle at least in partby: identifying, based at least in part on at least one of the firstsensor information or the second sensor information, an object in theenvironment; and determining a probability that the object is correctlyidentified, the probability being determined at least in part on atleast one of the second sensor information, past sensor anomalies, pastsensor failures, GPS data or log file data.
 12. The system of claim 10,the computing system being further configured to determine the impact onthe perception of the autonomous vehicle at least in part by: generatingprobabilistic map tiles associated with the environment based at leastin part on the first sensor information and the second sensorinformation.
 13. The system of claim 10, the computing system beingfurther configured to: determine one or more trajectories upon which theautonomous vehicle may traverse to compensate for the sensor anomaly orthe sensor failure, the one or more trajectories being based, at leastin part, on log file data associated with previous navigation of theautonomous vehicle.
 14. The system of claim 13, the computing systembeing further configured to: send the one or more trajectories to ateleoperator; and receive, from the teleoperator, an indication of afirst trajectory from the one or more trajectories.
 15. The system ofclaim 10, the computing system being further configured to: determinethe impact on the perception of the autonomous vehicle at least in partby determining a blind spot associated with a portion of theenvironment, wherein the sensor recovery strategy at least in partincludes, for the portion of the environment, prioritizing the secondsensor information over the first sensor information.
 16. An autonomousvehicle comprising: a plurality of sensors disposed to sense one or moreobjects in an environment of the autonomous vehicle, the plurality ofsensors comprising a plurality of first sensors of a first sensor typeand at least one second sensor of a second sensor type, the plurality offirst sensors and the at least one second sensor being disposed todetect one or more parameters in an environment of the autonomousvehicle; and a controller communicatively coupled to the plurality ofsensors, the controller being configured to: receive an indication of asensor anomaly or a sensor failure associated with a first sensor of theplurality of first sensors; receive first sensor information from one ormore working and non-anomalous other first sensors of the plurality offirst sensors; receive second sensor information from the at least onesecond sensor; determine, based at least in part on the indication, thefirst sensor information received from the one or more working andnon-anomalous other first sensors of the plurality of first sensors, andthe second sensor information, an impact on a perception of theautonomous vehicle caused by the sensor anomaly or the sensor failureassociated with the first sensor; determine, based at least in part onthe impact on the perception of the autonomous vehicle, a sensorrecovery strategy to compensate for the sensor anomaly or the sensorfailure; and control a trajectory of the autonomous vehicle based atleast in part on the sensor recovery strategy.
 17. The autonomousvehicle of claim 16, wherein the anomaly is caused by a reflectivityphenomena, and the trajectory is selected to minimize the reflectivityphenomena.
 18. The autonomous vehicle of claim 17, wherein thereflectively phenomena comprises receiving direct sunlight and thetrajectory is selected to avoid or block the direct sunlight.
 19. Theautonomous vehicle of claim 16, wherein the trajectory is at least oneof: computed from one or more log files, the log files comprising dataobtained by the plurality of sensors about an environment substantiallysimilar to the environment; or computed from one or more simulations,the simulations comprising simulated data about a simulated environmentsubstantially similar to the environment.
 20. The autonomous vehicle ofclaim 16, the controller being further configured to: update map dataassociated with the environment based at least in part on the firstsensor information and the second sensor information, the trajectorybeing selected based at least in part on the updated map data.